WO2022210361A1 - Dispositif d'analyse et procédé d'analyse - Google Patents

Dispositif d'analyse et procédé d'analyse Download PDF

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Publication number
WO2022210361A1
WO2022210361A1 PCT/JP2022/014421 JP2022014421W WO2022210361A1 WO 2022210361 A1 WO2022210361 A1 WO 2022210361A1 JP 2022014421 W JP2022014421 W JP 2022014421W WO 2022210361 A1 WO2022210361 A1 WO 2022210361A1
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data
multidimensional
vector data
new
reference vector
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PCT/JP2022/014421
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English (en)
Japanese (ja)
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哲也 金田
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株式会社D’isum
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/02—Stethoscopes
    • A61B7/04—Electric stethoscopes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the present disclosure relates to an analysis apparatus and an analysis method for converting the movement of an object into numerical data, which is its frequency component, and analyzing it.
  • Patent Literature 1 A method has been proposed for determining abnormalities in an object that moves with time, such as a mobile object (see Patent Document 1, for example).
  • frequency component data is categorized into normal and abnormal by clustering processing.
  • category classification it is necessary to learn in advance the relationship between frequency components and categories.
  • the purpose of the present disclosure is to make it possible to determine the possibility of an abnormality from numerical data having frequency components of movements of machines and people.
  • the analysis device and analysis method of the present disclosure are Holds a set of reference vector data obtained by converting target data with temporal movement into multidimensional vector data including frequency as a dimension, calculating a reference vector in a multidimensional space defined by the set of reference vector data;
  • the new data of the target is obtained, the new data is converted into new multidimensional vector data including frequency as a dimension;
  • the reference vector it is determined whether the position of the new multidimensional vector data in multidimensional space is within the region of the multidimensional space defined by the set of reference vector data.
  • the program of the present disclosure is a program for realizing a computer as each functional unit provided in the apparatus according to the present disclosure, and is a program for causing the computer to execute each step included in the method executed by the apparatus according to the present disclosure. .
  • FIG. 1 shows a system configuration example of the present disclosure; An example of the operation of the analysis device is shown. It is an example of a cluster in a multidimensional space and vector data that has begun to deviate from the cluster. An example of an algorithm for determining deviation from a cluster is shown. An example of reference vector data set ⁇ R i ⁇ , reference vector G and new multidimensional vector data X is shown.
  • FIG. 2 is an example of a two-dimensional representation of the distribution of multidimensional vector data of bearing vibration, that is, a reference map.
  • FIG. 4 is a flow chart showing an example of a data visualization method; A plot example of new multidimensional vector data X on the reference map is shown. 3 shows an example of distribution of multidimensional vector data of SAS. An example of multidimensional vector data of a plurality of motors provided in one machine is shown. An example of a state map is shown.
  • FIG. 1 shows an example of the system configuration of the present disclosure.
  • the analysis device 10 of the present disclosure includes a storage unit 11 that stores data and a signal processing unit 12 .
  • the analysis device 10 may include a communication section 13 and a display section 14 .
  • the analysis device 10 of the present disclosure can also be realized by a computer and a program, and the program can be recorded on a recording medium or provided through a network.
  • the present disclosure deals with data of temporally moving objects.
  • machine vibration and sound data detected by the sensor 20 can be exemplified. Since motion can be represented by frequency, data of an object with temporal motion can be converted into multi-dimensional vector data whose dimension is frequency.
  • the data targeted in the present disclosure is not limited to vibrations and sounds generated by machines, and can be applied to arbitrary data that can move, such as people and automobiles.
  • the analysis device 10 acquires data of an object that moves temporally and stores it in the storage unit 11 .
  • the target data may be acquired from the communication network 100 via the communication unit 13, but the present disclosure is not limited to this.
  • FIG. 2 shows an example of the operation of the analysis device 10.
  • the analysis device 10 generates a set ⁇ R i ⁇ of reference vector data from target data (S01), Calculate the reference vector G of ⁇ R i ⁇ on the multidimensional space, When new target data is acquired (S03), generating new multidimensional vector data X from new target data (S04); Using the reference vector G, the relationship between the reference vector data set ⁇ R i ⁇ and the new multidimensional vector data X is determined (S05).
  • the storage unit 11 stores data of an object that temporally moves.
  • the signal processing unit 12 divides the data into multiple segments. The division is by time relative to the data. As a result, a plurality of segments are generated by temporally dividing the motion.
  • the signal processing unit 12 generates multidimensional vector data having frequency components as dimensions for each segment (S01). As a result, a reference vector data set ⁇ R i ⁇ is generated and stored in the storage unit 11 . where i is the identifier of each segment.
  • the reference vector data forms one or more clusters in the multidimensional space, as shown in the distribution of normal data in FIG.
  • the data contained in the cluster generally exhibits a recursive movement on each dimension during normal operation, and therefore has a mountain-shaped distribution such as Gaussian distribution or binomial distribution.
  • the signal processing unit 12 calculates a reference vector G in a multidimensional space defined by the reference vector data set ⁇ R i ⁇ (S02).
  • the reference vector G is, for example, a centroid vector of a multidimensional vector composed of a set ⁇ R i ⁇ of reference vector data.
  • the new multidimensional vector data X is positioned within the cluster formed by the reference vector data set ⁇ R i ⁇ .
  • the new multidimensional vector data X begins to deviate from the normal data distribution area, like the abnormal data indicated by ⁇ in FIG.
  • the multidimensional data is represented on two dimensions using the dimensionality reduction technique. Therefore, the signal processing unit 12 determines the relationship between the reference vector data set ⁇ R i ⁇ and the new multidimensional vector data X.
  • the signal processing unit 12 determines whether the position of the new multidimensional vector data X in the multidimensional space is within the region of the multidimensional space defined by the reference vector data set ⁇ R i ⁇ .
  • the signal processing unit 12 determines the target corresponding to the new multidimensional vector data X. Determine that the state is different from the state of interest defined by the reference vector data set ⁇ R i ⁇ .
  • FIG. 4 shows an example of an algorithm executed by the signal processing section 12 in step S05.
  • the signal processing unit 12 calculates the reference vector G of the reference vector data set ⁇ R i ⁇ (S11).
  • the signal processing unit 12 uses the reference vector G to determine whether the position of the new multidimensional vector data X in the multidimensional space is within the multidimensional space region of the reference vector data set ⁇ R i ⁇ . It is determined whether or not (S12).
  • FIG. 5 shows an example of the reference vector data set ⁇ R i ⁇ , the reference vector G and the new multidimensional vector data X.
  • X is within the region of ⁇ R i ⁇
  • the component in the same direction as the vector (XG) connecting the dimension vector and the reference vector G can be compared with the length
  • the signal processing unit 12 determines that the new multidimensional vector data X is within the cluster if (XG, R i -G) ⁇
  • the signal processing unit 12 can determine that it is outside the cluster if ⁇ >0, and it can determine that it is inside the cluster if ⁇ 0.
  • a determination threshold ⁇ th is set in order to allow for a certain amount of error. It would be common to assume that
  • the simplest method is a method of "regarding a plurality of clusters as one cluster".
  • new data generally begin to deviate from any cluster. Therefore, even if a plurality of clusters are regarded as one cluster, at least part of the data can be judged to be abnormal by the above judgment method.
  • the above method can be applied to each cluster by calculating the reference vector G for each cluster in step S11.
  • This method is effective when a plurality of clusters are clearly separated, but cannot be said to be effective when the clusters overlap.
  • the target is an actual machine or the like, it is considered realistic to apply the simple method described above.
  • the present disclosure determines the relationship between the set of reference vector data ⁇ R i ⁇ and the new multidimensional vector data X using a geometric algorithm using the coordinates of the data in the multidimensional space. do.
  • the degree of freedom in designing the customer's operation system is greatly improved, and the cost can be greatly reduced.
  • the reference vector G is the centroid of the multidimensional vector data set ⁇ R i ⁇ .
  • the reference vector G is set in the outer region of the multidimensional vector data set ⁇ R i ⁇ .
  • the origin O of the multidimensional vector data forming the reference vector data is used as the reference vector.
  • the deterioration is almost always caused by an increase in the amplitude of the motion.
  • the change in amplitude is slight at the beginning of deterioration, the amplitude increases as deterioration progresses.
  • the shape of the spectrum also changes. In order to detect such movements, it is effective to use the origin, ie, the point where all frequency components are zero, as the reference vector.
  • FIG. 6 An example of the distribution of bearing vibration data is shown in FIG.
  • the amplitude of vibration is constant under normal conditions, so data is distributed on the surface of a multidimensional sphere in multidimensional space.
  • the spread of the distribution represents the variation of the frequency spectrum.
  • FIG. 6 shows the distribution of this data two-dimensionally.
  • ⁇ R i ⁇ be a set of normal reference vector data
  • R max be the R i with the maximum inner product (X, R i ).
  • a display to the effect that there is a possibility of abnormality may be displayed on the display unit 14 .
  • the stage at which the warning is issued is arbitrary, and the threshold for issuing the warning may be settable.
  • the divergence index ⁇ quantifies the degree of actual deterioration. Therefore, an alarm may be issued according to the divergence index ⁇ .
  • the signal processing unit 12 plots the reference vector data set ⁇ R i ⁇ on a two-dimensional plane to create a reference map. Furthermore, when new multidimensional vector data X is obtained, it is plotted on the reference map.
  • the overall algorithm is explained in FIG. Steps S01 to S05 shown in FIG. 7 are as described in the first embodiment.
  • the signal processing unit 12 When the signal processing unit 12 generates the reference vector data set ⁇ R i ⁇ (S01), the number of dimensions of the multidimensional reference vector data is reduced to two, and the reference vector data set ⁇ R i ⁇ is reduced to two dimensions. A reference map plotted on a plane is created (S21). Then, when the new multidimensional vector data X is generated (S04), the signal processing unit 12 calculates the plot position of the new multidimensional vector data X on the reference map (S22), and generates new multidimensional vector data X on the reference map. A two-dimensional map on which the dimensional vector data X is plotted is displayed on the display unit 14 (S23).
  • toorPIA or t-SNE T-distRibated Stochastic Neighbor Embedding
  • machine learning or any other dimensionality reduction algorithm
  • the method of plotting the two-dimensional vector data ⁇ corresponding to the new multidimensional vector data X on the reference map is arbitrary, and various methods can be adopted.
  • One method is to maintain the geometric relationship between the reference vectors G, R max and X in the multidimensional space on the two-dimensional map, and to point g and R max on the reference map corresponding to G This is a method of determining the point of the two-dimensional vector data ⁇ on the X reference map from the position of the corresponding point r max on the reference map.
  • R near that is angularly closest to the new multidimensional vector data X in the multidimensional space is extracted from the reference vector data set ⁇ R i ⁇ . Specifically, if the point on the reference map corresponding to R near is r near , the position of the two-dimensional vector data ⁇ on the reference map corresponding to X as shown in FIG. 8 is expressed below.
  • r near is a point corresponding to R near on the reference map.
  • SAS Sleep Apnea Syndrome
  • This technology can also be applied to detecting abnormalities in biological information and grasping conditions.
  • Sleep apnea syndrome SAS: Sleep Apnea Syndrome
  • SAS Sleep Apnea Syndrome
  • a major obstacle to the treatment of SAS is that simple examinations, which are the first step in determining the suitability of treatment, are troublesome and time consuming. This technology is effective in realizing a simple method that replaces this simple inspection.
  • the analysis apparatus 10 of the present embodiment acquires breath sounds recorded by a smartphone or the like during sleep, and divides the breath sounds into segments of about one minute, which are predetermined time widths. , segment by segment into frequency components and compute spectral data. Spectral data is multidimensional vector data, and segment data is distributed in multidimensional space.
  • the distribution range of the segment data of the healthy person is assumed to be the distribution range of the normal respiratory data.
  • a new subject's breath sound data is obtained, it is arranged at one point in this multidimensional space by converting it into vector data for each segment.
  • the center of gravity of the normal respiration data as a reference vector, it is possible to determine whether the subject data is within the distribution range of the normal respiration data (normal segment) or outside the distribution range (abnormal segment).
  • An indication of the subject's degree of SAS is obtained based on what percentage of the subject's total segment data in a given period of time is normal segments.
  • an area that is wider (or narrower) than the normal area to some extent may be used to determine the abnormal segment in order to determine that the subject's segment is abnormal.
  • Fig. 9 shows an example distribution of segment data for patients with various types of SAS and those without symptoms of SAS.
  • the center of gravity of the normal respiration data is used as a reference vector, and the multidimensional space data is displayed two-dimensionally by dimensionality reduction.
  • Segment abnormal breathing patterns include "obstructive type", "central type”, “hypopnea type”, and "mixed type", and in general, each type of segment is distributed in a different area in multidimensional space. can be done.
  • a subject's SAS type can also be determined based on which region the subject's segments are distributed.
  • a machine for example, a robot
  • the analysis device 10 of the present embodiment acquires load information such as current, voltage and torque of each motor provided in the machine, vibration and sound information, and the like, and converts them into multidimensional vector data.
  • load information such as current, voltage and torque of each motor provided in the machine, vibration and sound information, and the like
  • Fig. 10 shows an example of multidimensional vector data of each motor provided in one machine.
  • the figure shows an example of plotting on a two-dimensional plane according to the degree of similarity of each piece of multidimensional vector data.
  • the multidimensional vector data of each motor is distributed in defined areas M11, M21, M31, M41, M51, M61 in the multidimensional space.
  • a set of multi-dimensional vector data of each motor thus obtained becomes reference vector data of each motor.
  • Step 1) Create reference vector data for a plurality of motors for each machine (Fig. 10).
  • Step 2) When new target data is acquired, the "degree of divergence" of the new data from the reference vector data is calculated for each machine and each motor.
  • Step 3) Six-dimensional vector data whose dimension is the "degree of divergence" for each motor is created for each machine. In this embodiment, this 6-dimensional vector data is referred to as state vector data of the machine.
  • Step 4) If there are multiple machines, use each machine's set of state vector data to create a plane map. In this embodiment, this plane map is called a state map.
  • a value of each dimension of the state vector data represents an abnormality of each motor.
  • Step 5 Using the state vector data, it is visualized which of the 6 motors is worse than the others.
  • This disclosure can be applied to the information and communications industry.

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Abstract

L'objectif de la présente invention est de permettre de vérifier s'il existe ou non une possibilité d'anomalie à partir de données numériques comprenant des composantes de fréquence d'un mouvement de machine ou d'un humain.　La présente invention concerne un dispositif d'analyse. Le dispositif d'analyse stocke un ensemble de données de vecteur de référence obtenues par conversion de données concernant un sujet impliquant un mouvement temporel en données de vecteur multidimensionnel comprenant la fréquence en tant que dimension, calcule un vecteur de référence pour un espace multidimensionnel défini par l'ensemble de données de vecteur de référence, convertit, lors de l'acquisition de nouvelles données concernant le sujet, les nouvelles données en nouvelles données de vecteur multidimensionnel comprenant la fréquence en tant que dimension, et détermine, au moyen du vecteur de référence, si la position des nouvelles données de vecteur multidimensionnel dans l'espace multidimensionnel sont ou non situées dans la région de l'espace multidimensionnel définie par l'ensemble de données de vecteur de référence.
PCT/JP2022/014421 2021-04-02 2022-03-25 Dispositif d'analyse et procédé d'analyse WO2022210361A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030176788A1 (en) * 2002-01-28 2003-09-18 New Health Sciences, Inc. Detecting, assessing, and diagnosing sleep apnea
JP2014166572A (ja) * 2004-12-23 2014-09-11 Resmed Ltd 呼吸信号から呼吸パターンを検出して識別する方法
JP2020160852A (ja) * 2019-03-27 2020-10-01 株式会社ｔｏｏｒ 状態判定装置及び状態判定方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030176788A1 (en) * 2002-01-28 2003-09-18 New Health Sciences, Inc. Detecting, assessing, and diagnosing sleep apnea
US20050171432A1 (en) * 2002-01-28 2005-08-04 Crutchfield Kevin E. Detecting, assessing, and diagnosing sleep apnea
JP2014166572A (ja) * 2004-12-23 2014-09-11 Resmed Ltd 呼吸信号から呼吸パターンを検出して識別する方法
JP2020160852A (ja) * 2019-03-27 2020-10-01 株式会社ｔｏｏｒ 状態判定装置及び状態判定方法

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Title
"Health'ism. Health risk assessment service", D-ISUM. EXPERTISE FOR EVERYONE, D'ISUM, TOKYO, JP, 24 January 2021 (2021-01-24), Tokyo, JP, XP009540135, Retrieved from the Internet <URL:https://web.archive.org/web/20210124101652/https://d-isum.net/services/healthism/> [retrieved on 20221026] *
SUZUKI, YUSUKE; EMOTO, TAKAHIRO, AKUTAGAWA, MASATAKE: "Clustering the snoring sound using Self-Organizing Map", IEICE TECHNICAL REPORT, DENSHI JOUHOU TSUUSHIN GAKKAI, JP, vol. 109, no. 460 (MBE2009-101), 1 March 2010 (2010-03-01), JP , pages 7 - 11, XP009540069, ISSN: 0913-5685 *

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