Estimation device and estimation method

US11428612B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-11428612-B2
Application numberUS-202017022833-A
CountryUS
Kind codeB2
Filing dateSep 16, 2020
Priority dateSep 16, 2020
Publication dateAug 30, 2022
Grant dateAug 30, 2022

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Abstract

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A measurement device is configured to set an observation surface on a surface of a structure as a measurement surface to measure a change of the measurement surface as a measurement surface change vector. An estimator is configured to generate an estimation model based on a shape model obtained by modeling a shape of the structure. The estimator is configured to acquire a coefficient vector by solving a norm minimization problem by setting, as parameters, a measurement surface change vector and a part of the estimation model. The coefficient vector forms a sparse solution. The estimator is configured to estimate a change of a crack occurrence surface by determining a candidate surface, which is inside the structure and assumed to have a crack, as the crack occurrence surface, based on the coefficient vector and another part of the estimation model.

First claim

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What is claimed is: 1. An estimation device to estimate a state of a crack on a physical structure within an electric machine, the estimation device comprising: a measurement device configured to set an observation surface on a surface of the physical structure within the electric machine as a measurement surface to measure a change of the measurement surface as a measurement surface change vector; and an estimator, implemented by processing circuitry, configured to estimate the crack on the physical structure within the electric machine based on a change of the measurement surface measured by the measurement device, by estimating a change of a crack occurrence surface by determining a candidate surface, which is inside the physical structure within the electric machine and assumed to have the crack, as the crack occurrence surface, based on: a coefficient vector forming a sparse solution acquired by solving a norm minimization problem by setting, as parameters, the measurement surface change vector and a part of an estimation model, which is generated from a shape model obtained by modeling a shape of the physical structure within the electric machine; and another part of the estimation model. 2. The estimation device according to claim 1 , wherein the estimation device includes: a model generator configured to perform structure analysis based on a boundary condition set in advance for the shape model generated based on the measurement surface and the crack occurrence surface, to thereby generate the estimation model including a plurality of measurement surface estimated change vectors each estimating a change of the measurement surface and a plurality of crack occurrence surface estimated change vectors each estimating a displacement change of the crack occurrence surface as the change of the crack occurrence surface; a similar vector extractor configured to: set, as a measurement surface similar change vector, a measurement surface estimated change vector having a similarity with the measurement surface change vector higher than a reference similarity set in advance; set, as a crack occurrence surface similar change vector, a crack occurrence surface estimated change vector corresponding to the measurement surface similar change vector; and extract the measurement surface similar change vector serving as the part of the estimation model and the crack occurrence surface similar change vector serving as the another part of the estimation model; a feature extractor configured to extract the coefficient vector by solving an L1 norm minimization problem as the norm minimization problem based on the measurement surface change vector and the measurement surface similar change vector; and a crack analyzer configured to estimate a change in distribution of displacement changes of the crack occurrence surface based on the coefficient vector and the crack occurrence surface similar change vector. 3. The estimation device according to claim 2 , wherein the similar vector extractor is configured to use a cosine similarity as the similarity. 4. The estimation device according to claim 2 , wherein the model generator is configured to: divide the crack occurrence surface into a plurality of sections; set a plurality of nodes forming the respective plurality of sections as the crack; and estimate a displacement change of each of the plurality of nodes as the crack occurrence surface estimated change vector. 5. The estimation device according to claim 4 , wherein the model generator is configured to focus on displacement changes of a plurality of nodes forming a plurality of sections, which are continuously adjacent to one another in a partial region of the crack occurrence surface among the plurality of sections. 6. The estimation device according to claim 2 , wherein the model generator is configured to model the shape model as a model in a cylindrical coordinate system. 7. The estimation device according to claim 2 , wherein the measurement device is configured to perform the measurement under a state in which a load is applied to the physical structure within the electric machine before the generation of the shape model. 8. The estimation device according to claim 2 , wherein the model generator is configured to estimate a change in load of the crack occurrence surface as the change of the crack occurrence surface. 9. The estimation device according to claim 1 , wherein the measurement device is configured to measure, as the change of the measurement surface, at least one of a displacement change, a strain change, or an angle change of the measurement surface. 10. An estimation method to estimate a state of a crack on a physical structure within an electric machine, the estimation method comprising: setting, by a measurement device, an observation surface on a surface of the physical structure within the electric machine as a measurement surface to measure a change of the measurement surface as a measurement surface change vector; and estimating, by processing circuitry, the crack on the physical structure within the electric machine based on a change of the measurement surface measured by the measurement device, by estimating a displacement change of a crack occurrence surface, which is inside the physical structure within the electric machine based on: a coefficient vector forming a sparse solution acquired by solving a norm minimization problem by setting, as parameters, the measurement surface change vector and a part of an estimation model, which is generated from a shape model obtained by modeling a shape of the physical structure within the electric machine; and another part of the estimation model. 11. The estimation method according to claim 10 , wherein the estimating a displacement change includes: performing structure analysis based on a boundary condition set in advance for the shape model generated based on the measurement surface and the crack occurrence surface, to thereby generate the estimation model including a plurality of measurement surface estimated change vectors each estimating a change of the measurement surface and a plurality of crack occurrence surface estimated change vectors each estimating the displacement change of the crack occurrence surface; setting, as a measurement surface similar change vector, a measurement surface estimated change vector having a similarity with the measurement surface change vector higher than a reference similarity set in advance, setting, as a crack occurrence surface similar change vector, a crack occurrence surface estimated change vector corresponding to the measurement surface similar change vector and extracting the measurement surface similar change vector serving as the part of the estimation model and the crack occurrence surface similar change vector serving as the another part of the estimation model; extracting the coefficient vector by solving an L1 norm minimization problem as the norm minimization problem based on the measurement surface change vector and the measurement surface similar change vector; and estimating a distribution of displacement changes of the crack occurrence surface based on the coefficient vector and the crack occurrence surface similar change vector. 12. The estimation method according to claim 11 , further comprising: acquiring an amount of expansion of the crack on the crack occurrence surface based on a load applied to the physical structure within the electric machine and a physical property value of the physical structure within the electric machine; and determining a remaining usage period of the physical structure within the electric machine based on the amount of expansion

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Classifications

  • G01N3/066Primary

    with electrical indicating or recording means · CPC title

  • by means of external apparatus, e.g. test benches or portable test systems (G01M5/005 takes precedence) · CPC title

  • by applying steady bending forces (G01N3/26, G01N3/28 take precedence) · CPC title

  • by applying steady tensile or compressive forces (G01N3/28 takes precedence) · CPC title

  • Calculations a priori without experimental data · CPC title

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What does patent US11428612B2 cover?
A measurement device is configured to set an observation surface on a surface of a structure as a measurement surface to measure a change of the measurement surface as a measurement surface change vector. An estimator is configured to generate an estimation model based on a shape model obtained by modeling a shape of the structure. The estimator is configured to acquire a coefficient vector by …
Who is the assignee on this patent?
Mitsubishi Electric Corp, Mitsubishi Electric Res Laboratories Inc
What technology area does this patent fall under?
Primary CPC classification G01N3/066. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Aug 30 2022 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).