CN110470236A - A kind of flexible structure deformation reconstructing method being embedded in fiber grating - Google Patents
A kind of flexible structure deformation reconstructing method being embedded in fiber grating Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
- G01B11/165—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge by means of a grating deformed by the object
Abstract
The invention proposes a kind of flexible structure deformation reconstructing methods for being embedded in fiber grating to realize step for solving the larger technical problem of deformation reconstructed error existing in the prior art are as follows: (1) obtains target data HtWith source data Hs；(2) target data H is calculatedtImitative source data H 's；(3) training data H is obtained；(4) it establishes displacement and reconstructs pseudo- prediction model pτ；(5) it obtains Displacement-deformation and reconstructs equation；(6) the flexible structure deformation monitoring system of insertion fiber grating is established；(7) reconstruct displacement is solvedThe present invention gets rid of dependence of the modal method to dummy model, and reconstructed error is made to be no longer limited by the accuracy of dummy model, improves reconstructed error precision.
Description
Technical field
The invention belongs to Radar Antenna System fields, and in particular to a kind of flexible structure deformation reconstruct for being embedded in fiber grating
Method.
Background technique
The flexible structure for the certain keys developed in recent years, different degrees of will receive influence of the environment to its performance, than
It is such as mounted on the skin antenna of the body structure surface of aircraft, warship and armored vehicle, due to wind and snow in Service Environment, is vibrated and outer
Power impact etc. is some can not to resist factor, can all antenna array be caused to deform, and then cause the electrical property of skin antenna serious
Deteriorate even disablement.For the sensing technology of embedded structure matrix, the variation of sensing external environment carry
With the important function of information various in structure, fiber grating has high sensitivity, volume mass as a kind of novel sensor
Small, electromagnetism interference is easily formed the advantages that distributed network, and with fiber grating implantation, wavelength-division multiplex, space division multiplexing
Etc. the increasingly mature of technologies be widely used in the fields such as Radar Antenna System.In order to guarantee that the performance of flexible structure is wanted
It asks, needs to reconstruct displacement by insertion fiber grating, accurately monitor the deformation of flexible structure.
The flexible structure deformation reconstructing method of insertion fiber grating has KO method and based on modal theory method, is managed based on mode
It is to obtain modal coordinate by carrying out model analysis to structure, and then acquire reconstruct displacement by method.Since the displacement of reconstruct is
As the basis of follow-up work, it requires and obtain high-precision reconstruct displacement with a small amount of sensor is efficient.With regard to current skill
For art, restrict modal theory method reconstruction accuracy the main reason for be not directly available the modal information of flexible structure, and
It is to be extracted by the dummy model close with flexible structure, the difference between them will result in biggish reconstructed error, example
Such as: application publication number is CN 107103111A, entitled " the electronics function shape region feature point based on strain transducer
The Chinese invention patent application of shifting field reconstructing method ", this method is based on Modal Analysis Theory, in the feelings that structural loads information is unknown
Under condition, electronics function shape is reconstructed by the strain value that a small amount of strain transducer measures using strain-displacement transformational relation
The displacement field of region feature point.In practical applications, since there are gaps between dummy model and material object, so being based on modal theory
Strain-displacement transition matrix not can truly reflect mock-up measurement strain actual displacement between relationship, cause
Deformation reconstructed error is larger.
Summary of the invention
It is an object of the invention to overcome the problems of the above-mentioned prior art, propose it is a kind of insertion fiber grating it is soft
Property structural deformation reconstructing method, for solving the larger technical problem of deformation reconstructed error existing in the prior art.
To achieve the above object, the technical solution that the present invention takes includes the following steps:
(1) target data H is obtainedtWith source data Hs:
(1a) establishes the virtual mould all the same with the flexible structure material attribute of insertion fiber grating to be reconstructed and size
Type；
(1b) sets the number of the flexible structure deformation of insertion fiber grating to be reconstructed as M, utilizes fiber-optic grating sensor
The target strain value of M deformation of measurementThe displacement of targets value of M deformation is obtained using photogrammetric device measuringAnd it willWithIt is combined into target dataN group deformation test is carried out to dummy model simultaneously, obtains source dataWherein,Indicate the strain at the lower m sensor of jth time deformation to the flexible structure of insertion fiber grating
Value,J=1,2 ..., M,Indicate the lower n mesh of jth time deformation to the flexible structure of insertion fiber grating
The shift value of punctuate, t represent target data,Indicate the strain in dummy model at the lower m sensor of i-th deformation
Value, i=1,2 ..., N,The shift value of the lower n target point of i-th deformation in expression dummy model, behalf source data,
M < < N；
(2) target data H is calculatedtImitative source data H 's:
(2a) carries out model analysis using dummy model of the modal method to the flexible structure of insertion fiber grating, is strained
Mode ψ and displacement modesAnd by ψ andCalculate strain-displacement transition matrix T, calculation formula are as follows:
T∈Rn×m, then constructed by T, fs(x)=[TxT]T, wherein (ψTψ)-1It indicates to ψTψ inverts, ψTExpression seeks transposition to ψ,
X is independent variable, xTIndicate the transposition to x, [TxT]TIt indicates to xTThe result transposition of premultiplication T；
(2b) is by target data HtInIt is brought into displacement reconstruct anticipation function fs(x) it in independent variable x, is exported
ValueAnd by strain valueWith output valveThe matrix H of composition 'sAs HtImitative source data,
(3) training data H is obtained:
(3a) is by imitating source data H 'sAnd shift valueConstruct optimization object function And it solvesIn weight matrix Wherein,
(3b) passes through source data HsAnd weight matrixCalculating approaches displacement of targets valuePseudo- source shift value And it willWith HsInForm pseudo- target dataWhereinExpression source number
According to HsMultiplied by weight matrix
(3c) is to pseudo- target dataWith target data HtIt merges, obtains training data H,H∈R(N+M)×(m+n)；
(4) it establishes displacement and reconstructs pseudo- prediction model pτ:
(4a) sets the number of iterations as τ, and the coefficient of τ is wτ, wτ∈R1×(N+M), wτIn k-th of element be Maximum number of iterations is J, and enables τ=1；
(4b) is by wτInput of the 1st Dao the m column as extreme learning machine algorithm in H, by wτM+1 to n column conduct in H
The output of extreme learning machine algorithm calculates displacement and reconstructs pseudo- prediction model pτ(ε), wherein ε indicates pτThe input of (ε), ε ∈ R1 ×m；
(4c) calculates alignment error μτ:
Calculate training error coefficient eτ, and pass through eτCalculate alignment error μτ:
eτ=E/Dτ
Wherein, E indicates predictive displacement in the τ times iterationTraining miss
Difference, It indicates's
1 norm,It indicates1 norm, DτIndicate predictive displacement in the τ times iterationMaximum training error,||qa-pτ(εa) | | indicate qa-pτ(εa) 1 norm, | | qa| | indicate qa1 model
Number,Indicate eτIn k-th of element；
(4d) judges μτWhether >=0.5 or τ >=J is true, if so, the p that step (4b) is obtainedτ(ε) is used as trained position
It moves and reconstructs pseudo- prediction model pτ, otherwise, execute step (4e)；
(4e) enables τ=τ+1, while to coefficient wτIt is updated, and executes step (4b), wherein wτMore new formula are as follows:
Wherein, ατ-1=μτ-1/(1-μτ-1),BτIndicate normaliztion constant,Indicate eτ
In k-th of element；
(5) it obtains Displacement-deformation and reconstructs equation:
(5a) strains sourcePseudo- prediction model p is reconstructed as trained displacementτThe input of (ε) obtains predictive displacement value And by predictive displacement valueWith displacement of targets valueIt is combined into pseudo- displacement of targets data Source is displaced simultaneouslyWith output valveIt is combined into pseudo- source displacement data Then it calculatesWithBetween error delta,
(5b) is by source strain valueWith target strain valueAs the input of extreme learning machine algorithm, using error delta as pole
The output of learning machine algorithm is limited, error correction function is solvedAnd pass throughBuilding displacement reconstruct equation:Wherein,It indicatesInput,Indicate reconstruct displacement；
(6) the flexible structure deformation monitoring system of insertion fiber grating is established:
Establish the flexible structure deformation prison of the insertion fiber grating including data terminal, R-T unit and deformation monitoring center
Examining system, wherein the data terminal is demodulated for the wavelength change to fiber grating, to obtain strain information, and
It receives and is displaced by the reconstruct that the deformation monitoring center that R-T unit forwards obtains；The R-T unit is used for data terminal
The strain information of acquisition is sent to shape changing detection center, while the reconstruct displacement that shape changing detection center obtains is sent to data end
End；The shape changing detection center, for carrying out displacement reconstruct to strain information；
(7) reconstruct displacement is solved
Wave of the data terminal in flexible structure deformation monitoring system that (7a) passes through insertion fiber grating to fiber grating
Long variation is demodulated, and strain information is obtainedAnd deformation monitoring center is sent to by R-T unit；
(7b) will be strainedIt is input to displacement reconstruct equationIn, obtain the flexible knot of insertion fiber grating
The reconstruct of structure is displacedAnd it will by R-T unitIt is sent to data terminal.
Compared with the prior art, the invention has the following advantages:
1. the present invention uses majorized functionReduce source data HsWith target data HtBetween gap, obtain
Approach displacement of targets valuePseudo- source shift valueIt is equivalent to a small amount of target data HtProperty migrate to a large amount of
Source data Hs, then with coefficient wτFurther enhance target data HtInfluence power so that with dummy model extract source data Hs
It is more nearly target data Ht, it finally obtained displacement reconstruct equation to reconstruct displacement, and the prior art still passes through virtually
Model extracts modal information, reconstructs displacement using strain-displacement transition matrix T, and do not do into one the data of extraction
Step processing, so the present invention compared to existing technologies, gets rid of the reconstructing method based on modal theory to dummy model
It relies on, so that reconstruction accuracy is no longer limited by the accuracy of dummy model, reduce reconstructed error；
2. the present invention using insertion fiber grating flexible structure deformation monitoring system in R-T unit, can will to weight
The strain information of structure structure is sent to inspection center from data terminal, and the displacement of reconstruct is sent back to data end from inspection center
The real-time Transmission of data may be implemented in end, and the prior art can only carry out strain information acquisition and displacement reconstruct, affect reconstruct
Efficiency, and work limit will be reconstructed in ground, so the present invention is compared to existing technologies, it can more efficiently, accurately
Reconstruct displacement is obtained, is laid the foundation for work such as later remote health monitorings.
Detailed description of the invention
Fig. 1 is implementation flow chart of the invention.
Fig. 2 is the structural schematic diagram of the flexible structure deformation monitoring system for the insertion fiber grating that the present invention uses.
Fig. 3 is the experimental situation figure of the present invention with the comparison of prior art reconstruction accuracy.
Specific embodiment
Below in conjunction with the drawings and specific embodiments, invention is further described in detail.
Referring to Fig.1, the present invention includes the following steps:
Step 1) obtains target data HtWith source data Hs:
Step 1a) it establishes and all the same virtual of the to be reconstructed flexible structure material attribute for being embedded in fiber grating and size
Model can make dummy model be substantial access to the flexible structure of insertion fiber grating to be reconstructed in this way, acquire out dummy model
Modal information, and use the reconstructing method based on modal theory, calculate transition matrix T；
Step 1b) set insertion fiber grating to be reconstructed flexible structure deformation number as M, utilize optical fiber grating sensing
Device measures the target strain value of M deformationThe displacement of targets value of M deformation is obtained using photogrammetric device measuringAnd
It willWithIt is combined into target dataN group deformation test is carried out to dummy model simultaneously, obtains source dataWherein,Indicate the strain at the lower m sensor of jth time deformation to the flexible structure of insertion fiber grating
Value,J=1,2 ..., M,Indicate the lower n mesh of jth time deformation to the flexible structure of insertion fiber grating
The shift value of punctuate, t represent target data,Indicate the strain in dummy model at the lower m sensor of i-th deformation
Value, i=1,2 ..., N,The shift value of the lower n target point of i-th deformation in expression dummy model, behalf source data,
M < < N, because being limited to the influence of environment and condition, M deformation is conditional, and for dummy model, can be with
Remote super M times deformation test is carried out, this is also that cannot directly measure displacement, needs to reconstruct equation come one of the reason of reconstruct；
Step 2) calculates target data HtImitative source data H 's:
Step 2a) use modal method to carry out model analysis to the dummy model of the flexible structure of insertion fiber grating, it obtains
Strain mode ψ and displacement modesAnd by ψ andCalculate strain-displacement transition matrix T, calculation formula are as follows:T∈Rn×m, then constructed by T, fs(x)=[TxT]T, wherein (ψTψ)-1It indicates to ψTψ inverts,
ψTExpression seeks transposition to ψ, and x is independent variable, xTIndicate the transposition to x, [TxT]TIt indicates to xTThe result transposition of premultiplication T；
Step 2b) by target data HtInIt is brought into displacement reconstruct anticipation function fs(x) it in independent variable x, obtains defeated
It is worth outAnd by strain valueWith output valveThe matrix H of composition 'sAs HtImitative source data,Because of source data HsWith target data HtMatrix cause diversified in specifications, cannot do directly
Similar migration be converted into the imitative source data H ' with source data property so needing to handle datas, and and mesh
Mark data HtSpecification is identical；
Step 3) obtains training data H:
Step 3a) by imitating source data H 'sAnd shift valueConstruct optimization object function And it solvesIn weight matrix Wherein,When constructing dummy model inevitably and wait reconstruct knot
Structure generates difference, in order to eliminate this species diversity, more effectively utilizes more source data Hs, correct based on modal theory
Reconstructing method bring error makes imitative source data H 'sClose to displacement of targetsJust construct optimization object function
Step 3b) pass through source data HsAnd weight matrixCalculate the pseudo- source shift value approached And it willWith HsInForm pseudo- target dataWhereinExpression source
Data HsMultiplied by weight matrixGive source data HsMultiplied by weight coefficientAfterwards, its generation can be made close and displacement of targets valueSource shift value
Step 3c) to pseudo- target dataWith target data HtIt merges, obtains training data H,H∈R(N+M)×(m+n), by a large amount of source data HsIt is converted to and source data HtSimilar data,
Also it is considered as migrating the property of target data to source data, takes full advantage of data；
Step 4) establishes displacement and reconstructs pseudo- prediction model pτ, pass through coefficient wτTo further enhance target data HtInfluence
Power:
Step 4a) the number of iterations is set as τ, the coefficient of τ is wτ, wτ∈R1×(N+M), wτIn k-th of element be Maximum number of iterations is J, and enables τ=1；
Step 4b) by wτInput of the 1st Dao the m column as extreme learning machine algorithm in H, by wτM+1 to n in H, which is arranged, to be made
For the output of extreme learning machine algorithm, calculates displacement and reconstruct pseudo- prediction model pτ(ε), wherein ε indicates pτThe input of (ε), ε ∈ R1 ×m；
Step 4c) calculate alignment error μτ:
Calculate training error coefficient eτ, and pass through eτCalculate alignment error μτ:
eτ=E/Dτ
Wherein, E indicates predictive displacement in the τ times iterationTraining miss
Difference, It indicates's
1 norm,It indicates1 norm, DτIndicate predictive displacement in the τ times iterationMaximum training error,||qa-pτ(εa) | | indicate qa-pτ(εa) 1 norm, | | qa| | indicate qa1 model
Number,Indicate eτIn k-th of element；
Step 4d) judge μτWhether >=0.5 or τ >=J is true, if so, the p that step (4b) is obtainedτ(ε) is used as and trains
Displacement reconstruct pseudo- prediction model pτ, otherwise, execute step (4e)；
Step 4e) τ=τ+1 is enabled, while to coefficient wτIt is updated, and executes step (4b), wherein wτMore new formula
Are as follows:
Wherein, ατ-1=μτ-1/(1-μτ-1),BτIndicate normaliztion constant,Indicate eτ
In k-th of element；
Step 5) obtains Displacement-deformation and reconstructs equation:
Step 5a) source is strainedPseudo- prediction model p is reconstructed as trained displacementτThe input of (ε), obtains prediction bits
Shifting value And by predictive displacement valueWith displacement of targets valueIt is combined into pseudo- displacement of targets data Source is displaced simultaneouslyWith output valveIt is combined into pseudo- source displacement data Then it calculatesWithBetween error delta,
Step 5b) by source strain valueWith target strain valueAs the input of extreme learning machine algorithm, using error delta as
The output of extreme learning machine algorithm solves error correction functionAnd pass throughBuilding displacement reconstruct equation:Wherein,It indicatesInput,It indicates reconstruct displacement, solves error correction functionIt is in order to the form by final displacement reconstruct equation characterization at modal method result and the sum of the margin of error, with specific function
To show the error of modal method；
Step 6) establishes the flexible structure deformation monitoring system of insertion fiber grating, and structure is as shown in Fig. 2, include data
The flexible structure deformation monitoring system of terminal, R-T unit and the insertion at deformation monitoring center fiber grating, wherein the number
It according to terminal, is demodulated for the wavelength change to fiber grating, to obtain strain information, and receives and forwarded by R-T unit
Deformation monitoring center obtain reconstruct displacement；The R-T unit, the strain information for obtaining data terminal are sent to
Shape changing detection center, while the reconstruct displacement that shape changing detection center obtains is sent to data terminal；The shape changing detection center,
For carrying out displacement reconstruct to strain information；The detection system of foundation facilitates long-range, quick realization displacement reconstruct, makes to be displaced
Reconstruct is no longer limited by room and time, lays the foundation for subsequent work；
Step 7) solves reconstruct displacement
Step 7a) pass through the data terminal being embedded in the flexible structure deformation monitoring system of fiber grating to fiber grating
Wavelength change is demodulated, and strain information is obtainedAnd deformation monitoring center is sent to by R-T unit；
Step 7b) it will strainIt is input to displacement reconstruct equationIn, obtain the flexibility of insertion fiber grating
The reconstruct of structure is displacedAnd it will by R-T unitIt is sent to data terminal.
Below in conjunction with specific experiment, technical effect of the invention is described further:
1, experiment condition and content:
Finite element analysis is carried out in ANSYS18.0, and algorithm routine is run at MATLAB R2017a, it is shown in Fig. 3
It is tested under experimental situation.
To the present invention and the existing electronics function shape region feature point displacement field reconstructing method based on strain transducer
Reconstruction accuracy compare verifying, the results are shown in Table 1.
2, analysis of experimental results:
Table 1
According to table 1 as can be seen that the present invention is better than the prior art in reconstructed error precision aspect, error is greatly reduced,
This is because the present invention uses majorized functionThe gap between dummy model and practical structures is reduced, by a small amount of mesh
Mark data HtProperty migrate to a large amount of source data Hs, using coefficient wτFurther enhance target data HtInfluence power, contracting
Error brought by the reconstructing method based on modal theory is subtracted, therefore, just will lead to reconstruct accuracy of the invention and be higher than now
There is technology.
Claims (3)
1. a kind of flexible structure deformation reconstructing method for being embedded in fiber grating, which comprises the steps of:
(1) target data H is obtainedtWith source data Hs:
(1a) establishes the dummy model all the same with the flexible structure material attribute of insertion fiber grating to be reconstructed and size；
(1b) sets the number of the flexible structure deformation of insertion fiber grating to be reconstructed as M, measures M using fiber-optic grating sensor
The target strain value of secondary deformationThe displacement of targets value of M deformation is obtained using photogrammetric device measuringAnd it willWith
It is combined into target dataN group deformation test is carried out to dummy model simultaneously, obtains source dataWherein,Indicate the strain at the lower m sensor of jth time deformation to the flexible structure of insertion fiber grating
Value,J=1,2 ..., M,Indicate the lower n mesh of jth time deformation to the flexible structure of insertion fiber grating
The shift value of punctuate, t represent target data,Indicate the strain in dummy model at the lower m sensor of i-th deformation
Value, i=1,2 ..., N,The shift value of the lower n target point of i-th deformation in expression dummy model, behalf source data,
M < < N；
(2) target data H is calculatedtImitative source data Hs':
(2a) carries out model analysis using dummy model of the modal method to the flexible structure of insertion fiber grating, obtains strain mode
ψ and displacement modesAnd by ψ andCalculate strain-displacement transition matrix T, T ∈ Rn×m, it is pre- that displacement reconstruct is then constructed by T
Survey function fs(x), fs(x)=[TxT]T, wherein x is independent variable, xTIndicate the transposition to x, [TxT]TIt indicates to xTPremultiplication T
Result transposition；
(2b) is by target data HtInIt is brought into displacement reconstruct anticipation function fs(x) in independent variable x, output valve is obtainedAnd by strain valueWith output valveThe matrix H of composition 'sAs HtImitative source data,
(3) training data H is obtained:
(3a) is by imitating source data H 'sAnd shift valueConstruct optimization object function And it solvesIn weight matrixWherein,
(3b) passes through source data HsAnd weight matrixCalculating approaches displacement of targets valuePseudo- source shift valueAnd it willWith HsInForm pseudo- target dataWhereinTable
Show source data HsMultiplied by weight matrix
(3c) is to pseudo- target dataWith target data HtIt merges, obtains training data H,H∈R(N+M)×(m+n)；
(4) it establishes displacement and reconstructs pseudo- prediction model pτ:
(4a) sets the number of iterations as τ, and the coefficient of τ is wτ, wτ∈R1×(N+M), wτIn k-th of element be
1≤k≤N+M, maximum number of iterations J, and enable τ=1；
(4b) is by wτInput of the 1st Dao the m column as extreme learning machine algorithm in H, by wτM+1 to n column in H are used as the limit
The output of learning machine algorithm calculates displacement and reconstructs pseudo- prediction model pτ(ε), wherein ε indicates pτThe input of (ε), ε ∈ R1×m；
(4c) calculates alignment error μτ:
Calculate training error coefficient eτ, and pass through eτCalculate alignment error μτ:
eτ=E/Dτ
Wherein, E indicates predictive displacement in the τ times iterationTraining miss
Difference,E∈R(N+M)×1,It indicates1 norm,It indicates1 norm, DτIndicate predictive displacement in the τ times iterationMaximum training error,||qa-pτ(εa) | | indicate qa-pτ(εa) 1 norm, | | qa| | indicate qa1 model
Number,Indicate eτIn k-th of element；
(4d) judges μτWhether >=0.5 or τ >=J is true, if so, the p that step (4b) is obtainedτ(ε) is as trained displacement weight
Structure puppet prediction model pτ, otherwise, execute step (4e)；
(4e) enables τ=τ+1, while to coefficient wτIt is updated, and executes step (4b), wherein wτMore new formula are as follows:
Wherein, ατ-1=μτ-1/(1-μτ-1),BτIndicate normaliztion constant,Indicate eτIn
K-th of element；
(5) it obtains Displacement-deformation and reconstructs equation:
(5a) strains sourcePseudo- prediction model p is reconstructed as trained displacementτThe input of (ε) obtains predictive displacement valueAnd by predictive displacement valueWith displacement of targets valueIt is combined into pseudo- displacement of targets dataSource is displaced simultaneouslyWith output valveIt is combined into pseudo- source displacement dataThen it calculatesWithBetween error delta,
(5b) is by source strain valueWith target strain valueAs the input of extreme learning machine algorithm, learn error delta as the limit
The output of machine algorithm solves error correction functionAnd pass throughBuilding displacement reconstruct equation:Its
In,It indicatesInput,Indicate reconstruct displacement；
(6) the flexible structure deformation monitoring system of insertion fiber grating is established:
Establish the flexible structure deformation monitoring system of the insertion fiber grating including data terminal, R-T unit and deformation monitoring center
System, wherein the data terminal is demodulated for the wavelength change to fiber grating, to obtain strain information, and is received
The reconstruct displacement obtained by the deformation monitoring center that R-T unit forwards；The R-T unit, for obtaining data terminal
Strain information be sent to shape changing detection center, while the reconstruct displacement that shape changing detection center obtains is sent to data terminal；
The shape changing detection center, for carrying out displacement reconstruct to strain information；
(7) reconstruct displacement is solved
The data terminal in flexible structure deformation monitoring system that (7a) passes through insertion fiber grating becomes the wavelength of fiber grating
Change is demodulated, and strain information is obtainedAnd deformation monitoring center is sent to by R-T unit；
(7b) will be strainedIt is input to displacement reconstruct equationIn, obtain the flexible structure of insertion fiber grating
Reconstruct displacementAnd it will by R-T unitIt is sent to data terminal.
2. a kind of flexible structure deformation reconstructing method for being embedded in fiber grating according to claim 1, which is characterized in that step
Suddenly strain described in (2a)-displacement transition matrix T, calculation formula are as follows:
Wherein, ψ is strain mode,For displacement modes, (ψTψ)-1It indicates to ψTψ inverts, ψTExpression seeks transposition to ψ.
3. a kind of flexible structure deformation reconstructing method for being embedded in fiber grating according to claim 1, which is characterized in that step
Suddenly data terminal described in (6), including acquisition module and demodulation module, acquisition module are used to acquire the soft of insertion fiber grating
The wavelength change of fiber grating in property structure；Wavelength change of the demodulation module for fiber grating is demodulated, to obtain strain
Information.
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| CN111288912A (en) * | 2020-03-24 | 2020-06-16 | 北京航空航天大学 | Fiber bragg grating deformation measurement method for airborne distributed POS |
| CN113049217A (en) * | 2021-03-29 | 2021-06-29 | 中国空气动力研究与发展中心设备设计与测试技术研究所 | Dynamic monitoring method for multi-state information of flexible plate structure of large wind tunnel |
| CN114894110A (en) * | 2022-03-24 | 2022-08-12 | 西安电子科技大学 | Method for calibrating deformation of intelligent skin antenna structure |
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