WO2023090193A1 - Dispositif de traitement d'informations, système de commande, procédé de traitement d'informations, et programme - Google Patents

Dispositif de traitement d'informations, système de commande, procédé de traitement d'informations, et programme Download PDF

Info

Publication number
WO2023090193A1
WO2023090193A1 PCT/JP2022/041433 JP2022041433W WO2023090193A1 WO 2023090193 A1 WO2023090193 A1 WO 2023090193A1 JP 2022041433 W JP2022041433 W JP 2022041433W WO 2023090193 A1 WO2023090193 A1 WO 2023090193A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
abnormality
model
information processing
transforming
Prior art date
Application number
PCT/JP2022/041433
Other languages
English (en)
Japanese (ja)
Inventor
利次 土井
篤淑 籔内
慎司 伊藤
Original Assignee
株式会社エヌ・ティ・ティ・データCcs
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 株式会社エヌ・ティ・ティ・データCcs filed Critical 株式会社エヌ・ティ・ティ・データCcs
Publication of WO2023090193A1 publication Critical patent/WO2023090193A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2131—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2123/00—Data types
    • G06F2123/02—Data types in the time domain, e.g. time-series data

Definitions

  • Embodiments relate to an information processing device, a control system, an information processing method, and a program.
  • ISO International Organization for Standard 532 defines loudness as an index in sound analysis. Loudness is a psychological quantity indicating the loudness of sound based on human hearing. In the sensory test, the loudness is used as an aid to judgment by the inspectors.
  • the judgment criteria based on the sensory test described above may depend on the subjectivity of the inspector. Therefore, different inspectors may obtain different determination results. That is, it is not possible to objectively determine the presence or absence of an abnormality in the mechanism by the sensory inspection described above.
  • the present invention has been made with a focus on the above circumstances, and its purpose is to provide means for automatically and objectively determining the presence or absence of an abnormality in the mechanism.
  • An information processing apparatus calculates a plurality of values each associated with a set of a frequency and a fluctuation frequency based on time-series data; and calculating a probability that the data contains an anomaly.
  • FIG. 1 is a block diagram showing an example of the configuration of a mechanism control system according to an embodiment;
  • 1 is a block diagram showing an example of a hardware configuration of a model learning device according to an embodiment;
  • FIG. 1 is a block diagram showing an example of a functional configuration of a model learning device according to an embodiment
  • FIG. FIG. 4 is a diagram showing an example of the relationship between a plurality of teacher feature quantity candidate maps and a teacher feature quantity map generated by the model learning device according to the embodiment
  • 4 is a flowchart showing an example of mechanism control processing in the mechanism control system according to the embodiment
  • 4 is a flowchart showing an example of abnormality determination processing in the abnormality determination device according to the embodiment
  • 4 is a flowchart showing an example of feature quantity map generation processing in the abnormality determination device according to the embodiment
  • 4 is a flowchart showing an example of model learning processing in the model learning device according to the embodiment
  • 4 is a flowchart showing an example of teacher feature quantity map generation processing in the model learning device according to the embodiment.
  • FIG. 1 is a block diagram showing an example of the configuration of the mechanism control system according to the embodiment.
  • the mechanism control system 1 includes a mechanism 2, a sensor 3, an abnormality determination device 4, a model learning device 5, and a control device 6.
  • Mechanism 2 is the sound source for abnormality determination.
  • the mechanism 2 is, for example, an electric seat of an automobile or a wind power generation facility in which a motor or the like is assembled.
  • Mechanism 2 may be assembled with a plurality of motors each having equivalent specifications.
  • Mechanism 2 generates sound and vibration when operating in a predetermined mode of operation.
  • the operation mode is, for example, a reclining operation, a forward/backward moving operation, a left/right moving operation, and the like. Sounds and vibrations from the mechanism 2 may vary depending on the operating mode of the mechanism and the presence or absence of anomalies within the mechanism.
  • the operating mode of mechanism 2 is controlled by a control signal CNT from controller 6 .
  • the control signal CNT contains, for example, information instructing the mechanism 2 to start, suspend and end the operating mode.
  • the sensor 3 is, for example, a microphone or a vibrometer.
  • a sensor 3 is arranged in the vicinity of or in contact with the mechanism 2 .
  • Sensor 3 measures sound or vibration generated by operation of mechanism 2 .
  • the sound or vibration measured by the sensor 3 is transmitted to the abnormality determination device 4 as time-series measurement data S.
  • the measurement data S may be either an analog signal or a digital signal.
  • the abnormality determination device 4 is an information processing device such as a personal computer.
  • the abnormality determination device 4 executes abnormality determination processing regarding the measurement data S based on the operation mode signal MODE from the control device 6 .
  • the operation mode signal MODE is a signal for identifying the operation mode of mechanism 2 .
  • the abnormality determination device 4 generates a determination history R and an abnormality notification signal ANOM as a result of the abnormality determination process.
  • the abnormality notification signal ANOM is a signal for notifying the control device 6 that the measurement data S is abnormal.
  • the determination history R is information indicating whether or not the measurement data S is abnormal.
  • the abnormality determination device 4 transmits an abnormality notification signal ANOM to the control device 6 and outputs the determination history R to the user. Details of the abnormality determination process will be described later.
  • the model learning device 5 is an information processing device such as a personal computer.
  • the model learning device 5 executes model learning processing.
  • the model learning device 5 generates a judgment model M as a result of model learning processing.
  • the model learning device 5 transmits the generated determination model M to the abnormality determination device 4 . Details of the model learning process will be described later.
  • the judgment model M is, for example, a mathematical model including a neural network.
  • the determination model M is used for abnormality determination processing in the abnormality determination device 4 .
  • a neural network in the decision model M includes multiple parameters.
  • a plurality of parameters in the judgment model M are optimized for abnormality judgment processing by model learning processing.
  • the judgment model M has, for example, different models for each operation mode of the mechanism 2 .
  • the control device 6 is a control terminal that mainly controls the operation of the mechanism 2.
  • the control device 6 is a test device for checking the operation of the electric seat in the pre-shipment inspection.
  • the control device 6 is a control device for real-time operation control and failure monitoring of the installed facility.
  • Control device 6 transmits an operation mode signal MODE to abnormality determination device 4 .
  • Control device 6 receives an abnormality notification signal ANOM from abnormality determination device 4 .
  • Control device 6 sends control signal CNT to mechanism 2 .
  • FIG. 2 is a block diagram showing an example of the hardware configuration of the abnormality determination device according to the embodiment.
  • the abnormality determination device 4 includes a control circuit 11, a storage 12, a communication module 13, a user interface 14, a drive 15, and a storage medium 16.
  • the control circuit 11 is a circuit that controls each component of the abnormality determination device 4 as a whole.
  • the control circuit 11 includes a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), and the like.
  • the storage 12 is an auxiliary storage device for the abnormality determination device 4.
  • the storage 12 is, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a memory card.
  • the storage 12 stores the measurement data S, the determination model M, and the like used for the abnormality determination process.
  • the storage 12 may store a program for executing the abnormality determination process.
  • the communication module 13 is a circuit used for transmitting and receiving data with the sensor 3 and with the model learning device 5.
  • the user interface 14 is a circuit for communicating information between the user and the control circuit 11 .
  • User interface 14 includes input and output devices.
  • the input device includes, for example, a touch panel and operation buttons.
  • Output devices include, for example, displays and printers. Also, the output device may include a lamp, a buzzer, or the like.
  • the drive 15 is a device for reading software stored in the storage medium 16.
  • the drive 15 includes, for example, a CD (Compact Disk) drive, a DVD (Digital Versatile Disk) drive, and the like.
  • the storage medium 16 is a medium that stores software by electrical, magnetic, optical, mechanical or chemical action.
  • the storage medium 16 may store a program for executing the abnormality determination process.
  • FIG. 3 is a block diagram showing an example of the hardware configuration of the model learning apparatus according to the embodiment.
  • model learning device 5 includes control circuit 21 , storage 22 , communication module 23 , user interface 24 , drive 25 and storage medium 26 .
  • the control circuit 21 is a circuit that controls each component of the model learning device 5 as a whole.
  • the control circuit 21 includes a CPU, RAM, ROM, and the like.
  • the storage 22 is an auxiliary storage device for the model learning device 5.
  • the storage 22 is, for example, an HDD, SSD, memory card, or the like.
  • the storage 22 stores data used for model learning processing.
  • the storage 22 may also store a program for executing model learning processing.
  • the communication module 23 is a circuit used for transmitting and receiving data to and from the abnormality determination device 4.
  • the user interface 24 is a circuit for communicating information between the user and the control circuit 21 .
  • User interface 24 includes input and output devices.
  • the input device includes, for example, a touch panel and operation buttons.
  • Output devices include, for example, displays and printers.
  • the drive 25 is a device for reading software stored in the storage medium 26.
  • the drive 25 includes, for example, a CD drive, a DVD drive, and the like.
  • the storage medium 26 is a medium that stores software by electrical, magnetic, optical, mechanical or chemical action.
  • the storage medium 26 may store a program for executing model learning processing.
  • FIG. 4 is a block diagram showing an example of the functional configuration of the abnormality determination device according to the embodiment.
  • the CPU of the control circuit 11 expands a program related to abnormality determination processing stored in the storage 12 or the storage medium 16 to the RAM. Then, the CPU of the control circuit 11 interprets and executes the program developed in the RAM.
  • the abnormality determination device 4 functions as a computer including a first conversion unit 31 , a spectrogram generation unit 32 , a second conversion unit 33 , a feature map generation unit 34 , a probability calculation unit 35 and a determination unit 36 .
  • the first conversion unit 31 is a functional block that converts the time-series measurement data S into the frequency domain.
  • the first converter 31 converts the measurement data S from analog signals to digital signals.
  • the first converter 31 divides the measurement data S converted into digital signals into data units each having a length of period T1.
  • the period T1 is, for example, several milliseconds to several tens of milliseconds.
  • Two data units adjacent in time series may have overlapping periods. Two data units adjacent in time series may not have periods that overlap each other.
  • the first transformation unit 31 can perform, for example, fast Fourier transformation (FFT) processing on the measurement data S for each data unit.
  • FFT fast Fourier transformation
  • the first conversion unit 31 may convert the measurement data S into the frequency domain by using a plurality of bandpass filters that transmit different frequency bands, such as octave band analysis.
  • the first conversion unit 31 transmits the result of conversion processing for each data unit to the spectrogram generation unit 32 .
  • the conversion process to the frequency domain is also called "first conversion process”.
  • the sampling width (frequency band) of the frequency domain in the first conversion processing of the first conversion unit 31 does not have to be divided into constant widths like the FFT processing.
  • the frequency band in the first conversion process of the first conversion unit 31 may be divided into 1/3 octave bands or bands based on the Mel scale.
  • application of the Bark scale to frequency bands is not preferable from the viewpoint of computer load.
  • the spectrogram generation unit 32 is a functional block that generates a spectrogram using the result of the first conversion processing by the first conversion unit 31.
  • the spectrogram generation unit 32 accumulates the results of the first conversion processing for each data unit by the first conversion unit 31 in time series over the period T2.
  • the period T2 is longer than the period T1.
  • the period T2 is, for example, several hundred milliseconds.
  • the spectrogram generator 32 generates a spectrogram based on the results of the multiple first conversion processes accumulated over the period T2.
  • the spectrogram generator 32 transmits the generated spectrogram to the second converter 33 .
  • FIG. 5 is a diagram showing an example of a spectrogram generated by the abnormality determination device according to the embodiment.
  • a spectrogram is a collection of three-dimensional data. The three components of each three-dimensional data in the spectrogram correspond to time, frequency, and signal strength (amplitude), respectively.
  • the X and Y axes correspond to time and frequency, respectively.
  • the magnitude of amplitude corresponding to a certain time width and frequency band is indicated by the shade of color for each grid.
  • a grid in a spectrogram is a rectangular area in the spectrogram that is delimited by one time span and one frequency band.
  • a plurality of grids (aligned in the Y direction) corresponding to a certain time width correspond to the results of the first conversion processing for one data unit.
  • a spectrogram is formed by arranging the results of the first conversion processing for such data units in time series (in the X direction) in a number corresponding to the period T2.
  • a spectrogram is a set of multiple data groups arranged in time series in corresponding frequency bands.
  • the second conversion unit 33 is a functional block that converts the spectrogram generated by the spectrogram generation unit 32 into the fluctuating frequency domain. Fluctuation frequency is a frequency representation of the time variation of the peak value in a signal that oscillates at a certain frequency.
  • the second conversion unit 33 divides the spectrogram into multiple data groups. A plurality of data groups correspond to frequency bands different from each other. That is, each of the multiple data groups includes multiple grids (aligned in the X direction) corresponding to a certain frequency band in the spectrogram.
  • the second conversion unit 33 can perform FFT processing for each data group, for example.
  • the second transforming unit 33 may transform the spectrogram into the variable frequency domain by using a plurality of bandpass filters that transmit different frequency bands, such as octave band analysis.
  • the second conversion unit 33 transmits the result of conversion processing for each data group to the feature map generation unit 34 .
  • the conversion processing to the variable frequency domain is also called "second conversion processing".
  • the sampling width (fluctuation frequency band) of the fluctuation frequency band in the second conversion processing of the second conversion unit 33 does not have to be divided into constant widths unlike the FFT processing.
  • the variable frequency band in the second conversion process of the second conversion unit 33 may be divided into 1/3 octave bands or bands based on the Mel scale.
  • application of the Bark scale to fluctuating frequency bands is not preferable from the viewpoint of computer load.
  • the second conversion unit 33 may perform each of the level correction process, the time masking process, the frequency masking process, and the time weighting process that are performed when calculating the loudness.
  • the level correction process, time masking process, frequency masking process, and time weighting process are unnecessary. Therefore, the second conversion unit 33 does not need to perform level correction processing, time masking processing, frequency masking processing, and time weighting processing.
  • the feature map generation unit 34 is a functional block that uses the result of the second conversion processing by the second conversion unit 33 to generate a feature map.
  • the feature quantity map generation unit 34 accumulates the results of the second conversion processing for all data groups generated from one spectrogram as feature quantities.
  • the feature quantity map generation unit 34 generates a feature quantity map by mapping the accumulated feature quantity.
  • the feature map generation unit 34 transmits the generated feature map to the probability calculation unit 35 .
  • FIG. 6 is a diagram showing an example of a feature quantity map generated by the abnormality determination device according to the embodiment.
  • a feature map is a set of three-dimensional data. Three components of each three-dimensional data in the feature quantity map respectively correspond to frequency, fluctuation frequency, and signal strength (amplitude).
  • the X and Y axes correspond to frequency and variation frequency, respectively.
  • the magnitude of the amplitude corresponding to a certain frequency band and the variable frequency band is indicated by the shade of color for each grid.
  • a grid in the feature map is a rectangular area in the feature map that is partitioned by one frequency band and one variable frequency band.
  • a plurality of grids (aligned in the Y direction) corresponding to a certain frequency band correspond to the result of the second transformation processing for one data group.
  • a feature quantity map is formed by arranging a plurality of results of the second conversion processing of such a data group in the frequency direction (in the X direction).
  • the probability calculation unit 35 is a functional block that calculates the probability that the measurement data S is abnormal.
  • the probability calculator 35 selects the determination model M corresponding to the operation mode of the mechanism 2 based on the operation mode signal MODE.
  • the probability calculation unit 35 inputs the feature quantity map to the selected determination model M.
  • the probability calculation unit 35 calculates the abnormality probability of the measurement data S based on the output result from the determination model M to which the feature quantity map is input.
  • the abnormality probability is, for example, a real number between 0 and 1 inclusive. For example, the higher the abnormality probability, the higher the probability that the measurement data S is abnormal.
  • the probability calculation unit 35 transmits the calculated abnormality probability to the determination unit 36 .
  • the judgment model M includes a neural network. More specifically, the neural network included in the judgment model M is, for example, a convolutional neural network (CNN).
  • CNN convolutional neural network
  • the feature quantity map is input to the determination model M as if it were image information.
  • the amplitude values in each grid of the feature map are handled by the determination model M in the same way as the pixel values in the image.
  • the determination unit 36 is a functional block that determines the presence or absence of an abnormality in the measurement data S based on the abnormality probability.
  • the determination unit 36 executes determination processing each time it receives an abnormality probability from the probability calculation unit 35 .
  • the determination unit 36 determines that the measurement data S is abnormal.
  • the condition may be, for example, that the abnormality probability is greater than or equal to a first threshold (eg, 0.5). Further, the condition may be, for example, that the number of times the abnormality probability becomes equal to or greater than the first threshold is equal to or greater than the second threshold. In this way, arbitrary conditions are applied according to the operation mode of the mechanism 2 and the characteristics of the mechanism 2 .
  • the determination unit 36 generates a determination history R in which the results of determination processing are arranged in chronological order, and outputs it to the user. Further, when it is determined that the measurement data S has an abnormality, the determination unit 36 generates an abnormality notification signal ANOM and transmits it to the control device 6 . Note that, when it is determined that the measurement data S has an abnormality, the determination unit 36 may notify the user of the abnormality by sounding a buzzer, lighting a lamp, or the like.
  • FIG. 7 is a diagram showing an example of determination history generated by the abnormality determination device according to the embodiment.
  • the example of FIG. 7 shows a case where the determination history R is output to the user as a graph in which the abnormality probabilities are arranged in chronological order.
  • the determination history R is updated in the time direction each time a new abnormality probability is calculated.
  • the determination unit 36 controls the abnormality notification signal ANOM at each timing from time t1 to t8. Send to device 6 .
  • the abnormality determination device 4 determines whether or not there is an abnormality in the mechanism 2 based on the measurement data S using the determination model M, and notifies the user and the control device 6 of the result. be able to. Then, the control device 6 can interrupt the operation mode of the mechanism 2 by receiving the abnormality notification signal ANOM from the abnormality determination device 4 .
  • Model Learning Device A functional configuration of the model learning device according to the embodiment will be described.
  • FIG. 8 is a block diagram showing an example of the functional configuration of the model learning device according to the embodiment.
  • the CPU of the control circuit 21 loads a program related to model learning processing stored in the storage 22 or the storage medium 26 into the RAM. Then, the CPU of the control circuit 21 interprets and executes the program developed in the RAM.
  • the model learning device 5 functions as a computer including a first conversion unit 41 , a spectrogram generation unit 42 , a second conversion unit 43 , a teacher feature map generation unit 44 , a probability calculation unit 45 and an update unit 46 .
  • the model learning device 5 also stores the teacher data set T and the pre-learning model M0.
  • the pre-learning model M0 is a mathematical model including a neural network.
  • the configuration of the pre-learning model M0 is the same as that of the judgment model M, except that the parameter values are different.
  • the pre-learning model M0 is a determination model in an initial state, that is, before the parameters are optimized for the abnormality determination process.
  • a teacher data set T includes multiple teacher data.
  • Each of the plurality of teacher data includes data and labels associated with each other.
  • the data in each teacher data is, for example, data obtained by measuring the sound or vibration from the mechanism 2 in advance.
  • the length of data in each teacher data is, for example, period T3 or longer. Period T3 is longer than period T2. Therefore, a plurality of feature quantity maps can be generated from the data in each teacher data.
  • the label in each training data is information indicating the true judgment result for the corresponding data.
  • the labels within each training data include the anomaly probability of the corresponding data. That is, a label corresponding to data including abnormal sound or vibration indicates an abnormality probability of "1". On the other hand, a label corresponding to data that does not contain abnormal sound or vibration indicates an abnormality probability of "0".
  • the teacher data set T is processed by the model learning device 5 using teacher data as a processing unit.
  • the functional configuration of the model learning device 5 for each teacher data will be described below.
  • the functional configurations of the first conversion unit 41, the spectrogram generation unit 42, and the second conversion unit 43 are the first conversion unit 31, the spectrogram generation unit 32, and the It is equivalent to the second converter 33 .
  • the first conversion unit 41 is a functional block that converts time-series teacher data into the frequency domain.
  • the first conversion unit 41 divides the teacher data into data units having a length of period T1.
  • the first conversion unit 41 performs, for example, a first conversion process (for example, an FFT process) for each data unit on teacher data.
  • the first conversion unit 41 transmits the result of the first conversion process for each data unit to the spectrogram generation unit 42 . From the viewpoint of consistency between the judgment model M and the pre-learning model M0, it is desirable that the same frequency band as that of the first conversion unit 31 is applied as the frequency band in the first conversion processing of the first conversion unit 41 .
  • the spectrogram generation unit 42 is a functional block that generates a spectrogram using the result of the first conversion processing by the first conversion unit 41.
  • the spectrogram generation unit 42 accumulates the results of the first conversion processing for each data unit by the first conversion unit 41 in time series over the period T2.
  • the spectrogram generator 42 generates a spectrogram based on the results of a plurality of first conversion processes accumulated over the period T2.
  • the spectrogram generator 42 transmits the generated spectrogram to the second converter 43 .
  • the second conversion unit 43 is a functional block that converts the spectrogram generated by the spectrogram generation unit 42 into the fluctuating frequency domain.
  • the second conversion unit 43 divides the spectrogram into multiple data groups.
  • the second conversion unit 43 performs, for example, second conversion processing (for example, FFT processing) for each data group.
  • the second conversion unit 43 transmits the result of the second conversion processing for each data group to the teacher feature quantity map generation unit 44 . It should be noted that, from the viewpoint of consistency between the determination model M and the pre-learning model M0, the same fluctuation frequency band as that of the second conversion unit 33 may be applied to the fluctuation frequency band in the second conversion processing of the second conversion unit 43. desirable.
  • the teacher feature quantity map generation unit 44 is a functional block that uses the result of the second conversion processing by the second conversion unit 43 to generate a teacher feature quantity map.
  • the teacher feature quantity map generation unit 44 accumulates the results of the second conversion processing of all data groups generated from one spectrogram as teacher feature quantity candidates.
  • the teacher feature quantity map generating unit 44 generates a teacher feature quantity candidate map for each spectrogram by mapping the accumulated teacher feature quantity candidates. Then, the teacher feature quantity map generating unit 44 generates a teacher feature quantity map based on a plurality of teacher feature quantity candidate maps corresponding to a plurality of spectrograms.
  • the teacher feature quantity map generation unit 44 transmits the generated teacher feature quantity map to the probability calculation unit 45 .
  • FIG. 9 is a diagram showing an example of the relationship between a plurality of teacher feature quantity candidate maps generated by the model learning device according to the embodiment and the teacher feature quantity map. As shown in FIG. 9, for the amplitude value of each grid in the teacher feature quantity map, for example, the maximum value of the amplitude values in the corresponding grids of the plurality of teacher feature quantity candidate maps is applied.
  • model learning device 5 The functional configuration of the model learning device 5 will be described with reference to FIG. 8 again.
  • the functional configuration of the probability calculation unit 45 is the same as that of the probability calculation unit 35, except that the teacher feature map is input instead of the feature map, and that the pre-learning model M0 is used instead of the judgment model M. be.
  • the probability calculation unit 45 is a functional block that calculates the probability that teacher data is abnormal.
  • the probability calculation unit 45 inputs the teacher feature quantity map to the pre-learning model M0.
  • the probability calculation unit 45 calculates the abnormality probability of teacher data based on the output result from the pre-learning model M0 to which the teacher feature quantity map is input.
  • the probability calculator 45 transmits the calculated abnormality probability to the updater 46 .
  • the update unit 46 is a functional block that updates the parameters of the pre-learning model M0 based on the abnormality probability calculated by the probability calculation unit 45. Specifically, for example, the updating unit 46 calculates an evaluation function based on the calculated abnormality probability and label. For the evaluation function, for example, a function that minimizes when the calculated abnormality probability matches the label is applied. When the calculated evaluation function is equal to or greater than the threshold, the updating unit 46 updates the parameters of the pre-learning model M0. When updating the parameters, the updating unit 46 uses, for example, the error backpropagation method. The pre-learning model M ⁇ b>0 whose parameters have been updated by the update unit 46 is fed back to the probability calculation unit 45 . When all the evaluation functions calculated for the teacher data set T are less than the threshold, the update unit 46 transmits the pre-learning model M0 as the determination model M to the abnormality determination device 4.
  • the model learning device 5 can provide the abnormality determination device 4 with the determination model M optimized for the abnormality determination process.
  • FIG. 10 is a flowchart showing an example of mechanism control processing in the mechanism control system according to the embodiment.
  • the control device 6 transmits an operation mode signal MODE corresponding to the input operation mode to the abnormality determination device 4 (S1).
  • the operation mode signal MODE is used for selecting the determination model M in the abnormality determination device 4 .
  • control device 6 After the processing of S1, the control device 6 starts the operation of the mechanism 2 according to the input operation mode (S2).
  • the mechanism 2 After the processing of S2, the mechanism 2 starts the operation mode indicated by the control signal CNT.
  • the abnormality determination device 4 executes abnormality determination processing for the measurement data S based on the operation mode signal MODE transmitted in the process of S1.
  • the control device 6 waits until the input operation mode ends or until it receives the abnormality notification signal ANOM (S3).
  • the control device 6 determines whether or not the abnormality notification signal ANOM has been received (S4).
  • the processing of S4 is synonymous with the control device 6 determining whether or not the operation mode of the mechanism 2 has ended.
  • the control device 6 stops the operation of the mechanism 2 by interrupting the operation mode being executed ( S5).
  • the operation mode of the mechanism 2 can be automatically interrupted.
  • FIG. 11 is a flowchart showing an example of abnormality determination processing in the abnormality determination device according to the embodiment. In the example of FIG. 11, it is assumed that the abnormality determination device 4 has received the determination model M from the model learning device 5 in advance.
  • the probability calculation unit 35 selects the determination model M based on the received operation mode signal MODE (S11).
  • the abnormality determination device 4 waits until it starts receiving the measurement data S (S12).
  • the first conversion unit 31, the spectrogram generation unit 32, the second conversion unit 33, and the feature map generation unit 34 execute feature map generation processing (S13). Details of the feature map generation process will be described later.
  • the probability calculation unit 35 calculates an abnormality probability by inputting the feature quantity map generated in the process of S13 to the judgment model M (S14).
  • the determination unit 36 outputs the determination history R to the user based on the abnormality probability calculated by the process of S14 (S15).
  • the determination unit 36 determines whether or not there is an abnormality in the measurement data S based on the abnormality probability calculated by the process of S14 (S16).
  • the determination unit 36 transmits an abnormality notification signal ANOM to the control device 6 (S17).
  • the abnormality determination device 4 determines whether or not the reception of the measurement data S has ended (S18).
  • the first conversion unit 31, the spectrogram generation unit 32, the second conversion unit 33, and the feature map generation unit 34 receive the newly received measurement data S (S13). Then, following the process of S13, the processes of S14 to S18 are executed. In this way, the processes of S13 to S18 are repeated until the reception of the measurement data S is completed.
  • FIG. 12 is a flowchart showing an example of feature map generation processing in the abnormality determination device according to the embodiment.
  • the processing of S21 to S28 in FIG. 12 corresponds to the details of the processing of S13 in FIG.
  • the first conversion unit 31 waits until the measurement data S for the period T1 (that is, data unit) is accumulated (S21).
  • the first conversion unit 31 converts the accumulated measurement data S for the period T1 into the frequency domain (S22).
  • the first conversion unit 31 determines whether or not the measurement data S for the period T2 has been converted by the process of S22 (S23).
  • the first conversion unit 31 waits until new measurement data S for the period T1 is accumulated (S21). Then, when the measurement data S for the period T1 is accumulated, the first converter 31 executes the process of S22. In this manner, the processes of S21 and S22 are repeated until the measurement data S for the period T2 are converted by the process of S22.
  • the spectrogram generator 32 When the measurement data S for the period T2 has been converted (S23; yes), the spectrogram generator 32 generates a spectrogram based on the results of the process of S22 executed multiple times over the period T2 in chronological order (S24). .
  • the second conversion unit 33 selects a frequency band from the frequency domain of the spectrogram generated in the process of S24 (S25).
  • the second transforming unit 33 transforms the time-series data (that is, data group) corresponding to the frequency band selected in the process of S25 from the spectrogram into the fluctuating frequency domain (S26).
  • the second conversion unit 33 determines whether or not all frequency bands have been selected from the spectrogram (S27).
  • the second conversion unit 33 selects an unselected frequency band from the frequency domain of the spectrogram generated in S24 (S25). Then, following the process of S25, the processes of S26 and S27 are executed. In this manner, the processes of S25 to S27 are repeated until the data groups corresponding to all frequency bands in the spectrogram are converted by the process of S26.
  • the feature map generation unit 34 performs a plurality of S26 processes on all data groups within the same spectrogram. Based on the results, A feature quantity map is generated (S28).
  • model learning processing in the model learning device according to the embodiment will be described.
  • FIG. 13 is a flowchart showing an example of model learning processing in the model learning device according to the embodiment.
  • the model learning device 5 pre-stores the teacher data set T and the pre-learning model M0.
  • the control circuit 21 selects teacher data from the teacher data set T (S31).
  • the first conversion unit 41, the spectrogram generation unit 42, the second conversion unit 43, and the teacher feature amount map generation unit 44 execute the teacher feature amount map generation process based on the teacher data selected in the process of S31 (S32 ). Details of the teacher feature quantity map generation processing will be described later.
  • the probability calculation unit 45 calculates an abnormality probability by inputting the teacher feature quantity map generated in the process of S32 to the pre-learning model M0 (S33).
  • the update unit 46 determines whether the abnormality probability calculated by the process of S33 satisfies the conditions for the label in the teacher data selected by the process of S31 (S34). For example, the updating unit 46 calculates an evaluation function based on the abnormality probability and the label. The updating unit 46 then compares the calculated evaluation function with a threshold value to determine the abnormality probability and label matching.
  • the updating unit 46 updates the pre-learning model M0 (S35).
  • the probability calculation unit 45 inputs the teacher feature map generated in the process of S32 to the pre-learning model M0 updated in the process of S35 (S33). Then, following the process of S33, the processes of S34 and S35 are executed. In this manner, the processes of S33 to S35 are repeated until it is determined that the abnormality probability satisfies the condition for the label.
  • the control circuit 21 determines that all the teacher data in the teacher data set T have been selected. (S36).
  • the control circuit 21 selects unselected teacher data from the teacher data set T (S31). Then, following the process of S31, the processes of S32 to S36 are executed. In this manner, the processes of S31 to S36 are repeated until all teaching data are selected.
  • the update unit 46 transmits the pre-learning model M0 as the determination model M to the abnormality determination device 4 (S37).
  • FIG. 14 is a flowchart showing an example of teacher feature map generation processing in the model learning device according to the embodiment.
  • the processing of S41 to S50 in FIG. 14 corresponds to the details of the processing of S32 in FIG.
  • the first conversion unit 41 selects a period T1 (ie, data unit) from the selected teacher data. (S41).
  • the first conversion unit 41 transforms the selected teacher data for the period T1 into the frequency domain (S42).
  • the first conversion unit 41 determines whether or not the teacher data for period T2 has been converted by the process of S42 (S43).
  • the first conversion unit 41 further selects unselected teacher data for period T1 (S41). Then, when the teacher data for the period T1 is selected, the first conversion unit 41 performs the process of S42 on the newly selected teacher data for the period T1. In this way, the processes of S41 and S42 are repeated until the teacher data for period T2 is converted by the process of S42.
  • the spectrogram generation unit 42 When the teacher data for period T2 has been converted (S43; yes), the spectrogram generation unit 42 generates a spectrogram based on the results of the process of S42 executed multiple times over period T2 in chronological order (S44).
  • the second conversion unit 43 selects a frequency band from the frequency domain of the spectrogram generated in the process of S44 (S45).
  • the second conversion unit 43 converts the time-series data (that is, the data group) corresponding to the frequency band selected in the process of S45 from the spectrogram into the fluctuation frequency domain (S46).
  • the second conversion unit 43 determines whether or not all frequency bands have been selected from the spectrogram (S47).
  • the second conversion unit 43 selects an unselected frequency band from the frequency domain of the spectrogram generated in S44 (S45). Then, following the process of S45, the processes of S46 and S47 are executed. In this way, the processes of S45 to S47 are repeated until the data groups corresponding to all frequency bands in the spectrogram are converted by the process of S46.
  • the teacher feature map generation unit 44 performs multiple processing of S46 on all data groups within the same spectrogram. , a teacher feature value candidate map is generated (S48).
  • the teacher feature quantity map generation unit 44 determines whether or not the teacher feature quantity candidate map has been generated over the entire period of the teacher data (S49).
  • the first conversion unit 41 converts the training data of the period not used for generating the teacher feature quantity candidate map into the period T1 minute (ie, data unit) is selected (S41). Then, following the process of S41, the processes of S42 to S49 are executed. In this way, the processing of S41 to S49 is repeated until the teacher feature quantity candidate map is generated over all the periods in the teacher data.
  • the teacher feature quantity map generation unit 44 If the teacher feature quantity candidate map is generated over all periods in the teacher data (S49; yes), the teacher feature quantity map generation unit 44 generates a teacher feature quantity map based on a plurality of teacher feature quantity candidate maps. (S50). Specifically, for example, the teacher feature quantity map generation unit 44 applies the maximum value of the amplitude values in the grids of the plurality of teacher feature quantity candidate maps to the corresponding grid of the teacher feature quantity map.
  • the first conversion unit 31, the spectrogram generation unit 32, the second conversion unit 33, and the feature amount map generation unit 34 of the abnormality determination device 4 are based on the measurement data S, and each of the frequency band and fluctuation A plurality of feature quantities associated with the set of frequency bands are calculated.
  • the probability calculation unit 35 calculates the abnormality probability of the measurement data S by inputting the plurality of calculated feature amounts to the determination model M.
  • the judgment model M a convolutional neural network is used.
  • the abnormality determination device 4 can automatically calculate the abnormality probability as an index for objective determination processing without the intervention of an inspector.
  • the first conversion unit 31 converts each of the plurality of data units in the measurement data S into the frequency domain.
  • the spectrogram generation unit 32 generates a plurality of data groups arranged in time series in corresponding frequency bands based on the result of the first conversion processing.
  • the second transforming unit 33 transforms each of the plurality of data groups into the fluctuating frequency domain.
  • the feature quantity map generator 34 maps the result of the second conversion process to the corresponding frequency band and fluctuation frequency band.
  • each of the plurality of data units is divided into frequency bands based on a scale other than the Bark scale.
  • each of the plurality of data groups is divided into varying frequency bands based on the scale other than the Bark scale.
  • a feature quantity map can be generated while omitting calculation of a psychological quantity such as loudness.
  • a psychological quantity such as loudness.
  • more rational and objective determination processing can be performed.
  • by omitting the processing requiring a large amount of calculation required for loudness calculation even when a general-purpose computer is applied to the abnormality determination device 4, it is possible to calculate a plurality of feature amounts in real time based on the measurement data S. can.
  • the determination unit 36 determines whether or not there is an abnormality in the measurement data S based on the calculated abnormality probability.
  • the abnormality determination device 4 can automatically output the determination history R and the notification of the abnormality to the user and transmit the abnormality notification signal ANOM to the control device 6 without the intervention of an inspector.
  • control device 6 suspends the operation mode of the mechanism 2 based on the abnormality notification signal ANOM. Thereby, when it is determined that the sound or vibration generated from the mechanism 2 is abnormal, the mechanism 2 can be automatically interrupted. In this way, the mechanism control system 1 can automatically perform everything from determining whether there is an abnormality to controlling the operation of the mechanism 2 .
  • the teacher feature quantity map generation unit 44 of the model learning device 5 generates a plurality of teacher feature quantity candidate maps corresponding to the plurality of feature quantity maps arranged in time series.
  • the teacher feature quantity map generation unit 44 generates a teacher feature quantity map by mapping the maximum value of a plurality of teacher feature quantity candidate maps to the corresponding grid.
  • labels can be associated one-to-one with all the data in the teacher data, the workload of labeling the data can be reduced.
  • the updating unit 46 updates the parameters of the pre-learning model M0 based on the anomaly probability calculated from the pre-learning model M0 to which the teacher feature quantity map is input, and the label. As a result, the abnormality probability calculated from the pre-learning model M0 can be brought closer to the label abnormality probability. Therefore, the model learning device 5 can provide the abnormality determination device 4 with the determination model M optimized for the abnormality determination process.
  • labels may be assigned on a one-to-one basis to portions of data in the training data for period T2.
  • the processing of S49 and S50 shown in FIG. 14 is omitted.
  • the teacher feature quantity candidate map generated in the process of S48 is used as the teacher feature quantity map.
  • control device 6 suspends the operation mode of the mechanism 2 when the abnormality notification signal ANOM is received has been described, but the present invention is not limited to this.
  • the control device 6 may perform various control operations such as shifting the mechanism 2 to the safe mode. Further, the control device 6 may notify the user of the abnormality in response to the abnormality notification signal ANOM.
  • the program for executing the abnormality determination operation is executed by the abnormality determination device 4, and the program for executing the model learning operation is executed by the model learning device 5.
  • the program for executing the abnormality determination operation and the program for executing the model learning operation may be executed by the same information processing device.
  • the program for executing the abnormality determination operation and the program for executing the model learning operation may be executed by computing resources on the cloud.
  • the present invention is not limited to the above-described embodiments, and can be variously modified in the implementation stage without departing from the gist of the present invention. Further, each embodiment may be implemented in combination as appropriate, in which case the combined effect can be obtained. Furthermore, various inventions are included in the above embodiments, and various inventions can be extracted by combinations selected from a plurality of disclosed constituent elements. For example, even if some constituent elements are deleted from all the constituent elements shown in the embodiments, if the problem can be solved and effects can be obtained, the configuration with the constituent elements deleted can be extracted as an invention.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Medical Informatics (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un moyen pour déterminer automatiquement et objectivement la présence ou l'absence d'une anomalie d'un mécanisme. Un dispositif de traitement d'informations selon un mode de réalisation comprend un processeur configuré pour exécuter : le calcul d'une pluralité de valeurs associées chacune à un ensemble d'une fréquence et d'une fréquence de fluctuation sur la base de données de série chronologique S; et l'entrée de la pluralité de valeurs dans un modèle M pour calculer une probabilité d'une anomalie incluse dans les données de série chronologique.
PCT/JP2022/041433 2021-11-16 2022-11-07 Dispositif de traitement d'informations, système de commande, procédé de traitement d'informations, et programme WO2023090193A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2021-186424 2021-11-16
JP2021186424A JP7230155B1 (ja) 2021-11-16 2021-11-16 情報処理装置、制御システム、情報処理方法、及びプログラム

Publications (1)

Publication Number Publication Date
WO2023090193A1 true WO2023090193A1 (fr) 2023-05-25

Family

ID=85330596

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2022/041433 WO2023090193A1 (fr) 2021-11-16 2022-11-07 Dispositif de traitement d'informations, système de commande, procédé de traitement d'informations, et programme

Country Status (2)

Country Link
JP (1) JP7230155B1 (fr)
WO (1) WO2023090193A1 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7386278B2 (ja) * 2022-02-28 2023-11-24 株式会社日立製作所 検査装置及び方法
JP7471025B1 (ja) 2023-06-05 2024-04-19 株式会社コジマプラスチックス 性能検査システム
CN116906839B (zh) * 2023-09-14 2023-12-01 浙江英集动力科技有限公司 融合物理测量和软测量的热力管线安全智能监测预警方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020064286A (ja) * 2018-10-12 2020-04-23 株式会社東陽テクニカ 異音等検出システム、装置、方法及びプログラム
US20210256991A1 (en) * 2020-02-11 2021-08-19 Purdue Research Foundation System and methods for machine anomaly detection based on sound spectrogram images and neural networks

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020064286A (ja) * 2018-10-12 2020-04-23 株式会社東陽テクニカ 異音等検出システム、装置、方法及びプログラム
US20210256991A1 (en) * 2020-02-11 2021-08-19 Purdue Research Foundation System and methods for machine anomaly detection based on sound spectrogram images and neural networks

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ICHIKAWA SHUTO, KUWAHARA, NORIAKI. : "A method of recognizing abnormal heart sound using CNN-LSTM network.", IPSJ SIG TECHNICAL REPORT (ASD), vol. 17, no. 7, 28 February 2020 (2020-02-28), pages 1 - 7, XP093068309 *
ISHIDA, KENJI; OKOSHI, MASARU: "Sound Quality Evaluation of Machine Sound and Natural Sound Using Variable Sound Analysis", PROCEEDINGS OF 2010 AUTUMN MEETING OF THE ACOUSTICAL SOCIETY OF JAPAN; SUITA, JAPAN; SEPTEMBER 14-16, vol. 2010, 2010, pages 1001 - 1004, XP009546409 *

Also Published As

Publication number Publication date
JP2023073761A (ja) 2023-05-26
JP7230155B1 (ja) 2023-02-28

Similar Documents

Publication Publication Date Title
WO2023090193A1 (fr) Dispositif de traitement d'informations, système de commande, procédé de traitement d'informations, et programme
US11568244B2 (en) Information processing method and apparatus
US10228994B2 (en) Information processing system, information processing method, and program
US10637751B2 (en) Methods and systems for online monitoring using a variable data sampling rate
CN105528652A (zh) 一种预测模型的建立方法及终端
CN112489682B (zh) 音频处理方法、装置、电子设备和存储介质
US20160357886A1 (en) System for analytic model development
US20230325292A1 (en) Anomaly detection using multiple detection models
WO2022134001A1 (fr) Procédé et système de développement d'infrastructure de modèle d'apprentissage automatique basés sur une technologie de conteneurisation
US20190266988A1 (en) Chord Identification Method and Chord Identification Apparatus
CN112463301A (zh) 基于容器的模型训练测试调优和部署方法和装置
US12105752B2 (en) Audio analysis method, audio analysis device and non-transitory computer-readable medium
CN116680146A (zh) 一种保障应用软件安全可靠运行的方法和装置
US20200160820A1 (en) Performance analysis method
CN110322587B (zh) 驾驶过程中的评价记录方法、装置、设备及存储介质
US12106770B2 (en) Sound model generation device, sound model generation method, and recording medium
CN112463957A (zh) 非结构化文本日志流的摘要提取方法和装置
JP2013250155A (ja) 発音判定装置、発音判定方法、及びプログラム
CN115185819A (zh) 系统测试方法、装置、设备及计算机可读存储介质
KR102456407B1 (ko) 애플리케이션 로딩 시간을 분석하는 시스템 및 방법
Ang et al. Emotional prosody analysis on human voices
EP4421572A1 (fr) Appareil de traitement d'informations, procédé de traitement d'informations et programme de traitement d'informations
WO2021130997A1 (fr) Dispositif de compression de modèle, système d'apprentissage, procédé de compression de modèle et support d'enregistrement de programmes
KR102680280B1 (ko) 절대음감 훈련을 제공하는 전자 장치, 절대음감 훈련 제공 방법, 및 컴퓨터 프로그램
CN115208057A (zh) 一种电力设备状态监控方法及电子设备

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22895475

Country of ref document: EP

Kind code of ref document: A1