CN109933872A - Based on the recognition methods of composite structure shock loading and device that enhancing is sparse - Google Patents
Based on the recognition methods of composite structure shock loading and device that enhancing is sparse Download PDFInfo
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Abstract
The disclosure discloses a kind of composite structure shock loading recognition methods sparse based on enhancing, comprising: obtains the transfer function matrix between composite structure position excited by impact and response point position；Measurement is applied to impulse response signal caused by the shock loading to be identified of composite structure；Construction is based on lpThe sparse regularization model of the enhancing of norm, and l is weighted using iteration1Norm Method, which solves, enhances sparse regularization model, and identification is applied to the shock loading of composite structure.The disclosure further discloses a kind of composite structure shock loading identification device sparse based on enhancing.Be based on l2The Tikhonov method of norm is compared, and disclosure stability is strong, is able to suppress amplification of the measurement noise in recognition result；Be based on l1The standardized sparse regularization method of norm is compared, and the disclosure is able to ascend the accuracy of identification of shock loading peak force, as a result also more sparse.
Description
Technical field
The disclosure belongs to composite material structure health monitoring field, and in particular to a kind of composite material sparse based on enhancing
Structural impact load recognition method and device.
Background technique
For composite structure, it is to constitute the significant threat of structure safety that mesh caused by shock loading, which can not examine damage,
One of with hidden danger.Composite structure such as commercial aviation engine blower blade, wing etc., can not in military service and maintenance process
Avoid by exotic such as hail, flying bird, maintenance tool etc. impact.When shock loading is impacted more than composite structure
When damage threshold, fatal " internal injury " that concealment is strong, harmfulness is big and mesh can not be examined can be formed in inside configuration, under damage accumulation
Integrality and bearing capacity to composite structure cause a hidden trouble, so that huge economic loss and personnel be caused to hurt
It dies.Therefore, in the monitoring structural health conditions of composite material, the shock loading that identification in time may cause damage has important meaning
Justice.
Load identification was designed originating from the aviation field of the 1970s due to the raising required aeroplane performance
A large amount of composite materials are used in journey, in order to preferably play composite structure load-carrying properties, it is desirable that accurately understand aircraft
Force-bearing situation in practical flight then proposes the research topic of load identification.Using between the vibratory response for being easy to measure
Ground connection realizes unknown Dynamic Load Identification, belongs to the second class indirect problem of Structural Dynamics, and Ill-posed characteristic means measurement response
In slight error this may result in identification dynamic loading substantial deviation true value.It is different from dynamic response solution direct problem, it carries
Lotus identification indirect problem is typical morbid state or ill-posed problem, that is, is unsatisfactory for Hadmard existence and uniqueness and stability three
Criterion, and need to add constraint condition by regularization method, well-posedness problem is converted by ill-posed problem.Nearly 30 years
Come, in l2Under norm regularization frame, by adding constraint condition, all kinds of regularization methods such as Tikhonov, truncated singular value
Decomposition, functional approaching etc. are widely applied to shock loading identification field, to overcome the ill-posedness of indirect problem.However, existing
Based on l2The regularization method of norm exist in terms of accuracy of identification, stability, computational efficiency, parameter bottleneck and
Limitation.Nearly 10 years, the promotion of compressed perception new theory was based on l1The sparse constraint of norm is as a basic regular conditions
By unprecedented concern, and rapidly become the Some Questions To Be Researched of signal, image procossing and related fields.Come from sparse angle
Say, shock loading with respect to its sampled data length be in time domain it is very sparse, only have near Impulsive load area larger
Value rather than loading zone is zero.It is currently based on l1The standardized sparse regularization method of norm has been applied in load identification field, so
And this method easily leads to the shock loading recognition result of " owing estimation ", and empty there is also being promoted in terms of the degree of rarefication of recognition result
Between.
Summary of the invention
In view of the above-mentioned problems, the disclosure is designed to provide a kind of composite structure impact load sparse based on enhancing
Lotus recognition methods, is able to solve based on l1The technical issues of standardized sparse regularization method " owing estimation " of norm, and solve
It is traditional based on l2The technical problem that the regularization method accuracy of identification of norm is low, solution is unstable.
The purpose of the disclosure is achieved by the following technical programs:
A kind of composite structure shock loading recognition methods sparse based on enhancing, includes the following steps:
S100: the transfer function matrix between composite structure position excited by impact and response point position is obtained；
S200: measurement is applied to impulse response signal caused by the shock loading to be identified of composite structure；
S300: l is based on based on step S100 and step S200 constructionpThe sparse regularization model of the enhancing of norm, and utilize
Iteration weights l1Norm Method, which solves, enhances sparse regularization model, and identification is applied to the shock loading of composite structure.
Preferably, step S100 includes the following steps:
S101: the frequency response function H (ω) between composite structure position excited by impact and response point position is obtained；
S102: inverse fast Fourier transform is carried out to frequency response function H (ω) and obtains unit impulse response function h (t), to list
The discrete acquisition transfer function matrix H of digit pulse receptance function h (t), wherein ω indicates that circular frequency variable, t indicate time variable.
Preferably, in step S101, the frequency response function H (ω) has by hammering method or by establishing composite structure
Limit meta-model simultaneously carries out harmonic responding analysis acquisition.
Preferably, in step S200, the impulse response signal is measured by vibrating sensor.
Preferably, step S300 includes the following steps:
S301: construction is based on lpThe sparse regularization model of the enhancing of norm:
Wherein, H indicates transfer function matrix；F indicates shock loading to be identified；Indicate residual error item,Indicate lpNorm regularization item or penalty function item；Norm p value range is p ∈ [0,1]；λ is indicated
Regularization parameter；The data length of n expression shock loading vector f；fiIndicate i-th of element in load vector f to be identified；|
|·||2Indicate the l of vector2Norm；
S302: it determines the value of norm p: as p=1, enhancing the convex Optimized model that sparse regularization model is standard；When
P ∈ [0,1) when, it is non-convex for enhancing sparse regularization model.
S303: initialization: regularization parameter λ=0.01 | | HTy||∞~0.5 | | HTy||∞, iteration ends threshold epsilon=10-6, weight adjusting parameter η(0)=0.00001~1, weight matrix is unit matrix W(0)=I, the number of iterations k=0；Wherein, H table
Show transfer function matrix；Y indicates impulse response signal；||·||∞Indicate Infinite Norm；The transposition of subscript T expression vector.
S304: it solves and is based on lpThe sparse regularization model of the enhancing of norm: the l that step S301 is constructedpNorm enhancing is dilute
Thin regularization model is converted to weighting l1Norm Model:
Wherein, min indicates to minimize；H indicates transfer function matrix；F indicates shock loading to be identified；Y indicates that impact is rung
Induction signal；Indicate residual error item；||W(k)f||1Indicate regularization term；||.||1Indicate the l of vector1Norm；λ is indicated just
Then change parameter；W(k)Indicate weight matrix when the number of iterations k；K indicates the number of iterations；Enable intermediate variable x=W(k)F, then wait know
Other load is represented by f=(W(k))-1X, then above formula is converted into the l of standard1Norm regularization model:
Wherein, y indicates impulse response signal；X indicates intermediate variable；λ indicates regularization parameter；Define intermediary matrix A=H
(W(k))-1。
S305: weight is updated
K indicates the number of iterations；
For ease of calculation, it can use weight adjusting parameter η(k)=η(0)。
S306: setting iteration weights l1Norm Method Stopping criteria, judges whether iteration restrains according to the following formula:
Wherein, if currently solving f(k+1)Meet above formula Stopping criteria, then terminate iterative process, obtains shock loading f；It is no
Then, the number of iterations k=k+1 is enabled, iterative process return step S304 continues to iterate to calculate, until meeting above formula.
Preferably, it in step S304, solves and is based on lpThe method of the sparse regularization model of enhancing of norm includes following appoints
It anticipates one kind: convex optimization method such as interior point method, gradient projection method and iteration threshold method.
The disclosure also provides a kind of composite structure shock loading identification device sparse based on enhancing, comprising:
Excitation vibration module, for obtaining composite structure position excited by impact and responding the biography between point position
Delivery function matrix；
Shock response measurement module is applied to punching caused by the shock loading to be identified of composite structure for obtaining
Hit response signal；
Load identification module is based on l for constructingpThe sparse regularization model of the enhancing of norm, and l is weighted using iteration1Model
Counting method, which solves, enhances sparse regularization model, and identification is applied to the shock loading of composite structure.
Preferably, the impulse response signal is measured by vibrating sensor.
Preferably, described to be based on lpThe sparse regularization model of the enhancing of norm are as follows:
Wherein, H indicates transfer function matrix；F indicates shock loading to be identified；Indicate residual error item,Indicate lpNorm regularization item or penalty function item；Norm p value range is p ∈ [0,1]；λ table
Show regularization parameter；The data length of n expression shock loading vector f；fiIndicate i-th of element in load vector f to be identified；
||·||2Indicate the l of vector2Norm.
Preferably, described to be based on lpThe sparse regularization model of the enhancing of norm can be exchanged into weighting l1Norm Model:
Wherein, min indicates to minimize；H indicates transfer function matrix；F indicates shock loading to be identified；Y indicates that impact is rung
Induction signal；Indicate residual error item；||W(k)f||1Indicate regularization term；||.||1Indicate the l of vector1Norm；λ is indicated just
Then change parameter；W(k)Indicate weight matrix when the number of iterations k；K indicates the number of iterations；Enable intermediate variable x=W(k)F, then wait know
Other shock loading is represented by f=(W(k))-1X, then above formula is converted into the l of standard1Norm regularization model:
Wherein, y indicates impulse response signal；X indicates intermediate variable；λ indicates regularization parameter；Define intermediary matrix A=H
(W(k))-1。
Compared with prior art, disclosure bring has the beneficial effect that
1, and based on l2The Tikhonov method of norm is compared, and disclosure stability is strong, is able to suppress measurement noise and is being known
Amplification in other result；
2, and based on l1The standardized sparse regularization method of norm is compared, and the disclosure is able to ascend shock loading peak force
Accuracy of identification is as a result also more sparse；
3, the iteration for the solution enhancing sparse model that the disclosure uses weights l1Norm Method, fast convergence rate, usual one
Secondary iteration can promote shock loading peak value accuracy of identification.
Detailed description of the invention
Fig. 1 is a kind of composite structure shock loading recognition methods flow chart sparse based on enhancing in the disclosure；
Fig. 2 is that a kind of composite panel structural impact load sparse based on enhancing that an embodiment of the present disclosure provides is known
The structural schematic diagram of other device；
Fig. 3 (a) to Fig. 3 (d) is the composite structure impact that acceleration transducer is surveyed in an embodiment of the present disclosure
Respond schematic diagram；Wherein, Fig. 3 (a) indicates measuring point R1；Fig. 3 (b) indicates measuring point R2；Fig. 3 (c) indicates measuring point R3；Fig. 3 (d) is indicated
Measuring point R4；
Fig. 4 (a) to Fig. 4 (d) is the acceleration that four different measuring points of composite structure are utilized in an embodiment of the present disclosure
Spend the result of signal difference inverting shock loading；Wherein, Fig. 4 (a) indicates measuring point R1；Fig. 4 (b) indicates measuring point R2；Fig. 4 (c) is indicated
Measuring point R3；Fig. 4 (d) indicates measuring point R4。
Specific embodiment
1 to Fig. 4 (d) technical solution of the disclosure is described in detail with embodiment, is implemented below with reference to the accompanying drawing
Example is merely exemplary, and is not intended as the restriction to the disclosure.
It is a kind of based on sparse composite structure shock loading recognition methods is enhanced referring to Fig. 1, include the following steps:
S100: the transfer function matrix between composite structure position excited by impact and response point position is obtained；
The step is realized by following process:
S101: it obtains frequency response function H (ω)；
Frequency response function H (ω) can be obtained by two ways: first is that being swashed using hammering method measurement composite structure impact
It encourages position and responds the frequency response function H (ω) between point position；Second is that establishing composite structure finite element model, pass through humorous sound
It should analyze and obtain frequency response function H (ω).
S102: inverse fast Fourier transform is carried out to frequency response function H (ω) and obtains unit impulse response function h (t), to list
The discrete acquisition transfer function matrix H of digit pulse receptance function h (t), wherein ω indicates that circular frequency variable, t indicate time variable.
S200: measurement is applied to impulse response signal caused by the shock loading to be identified of composite structure；
In the step, measured by using vibrating sensor (such as acceleration transducer, foil gauge, piezoelectric transducer PZT)
Impulse response signal y caused by composite structure shock loading.
S300: l is based on based on step S100 and step S200 constructionpThe sparse regularization model of the enhancing of norm, and utilize
Iteration weights l1Norm Method, which solves, enhances sparse regularization model, and identification is applied to the shock loading of composite structure.
The step comprises the following processes:
S301: construction is based on lpThe sparse regularization model of the enhancing of norm:
Wherein, H indicates transfer function matrix；F indicates shock loading to be identified；Indicate residual error item,Indicate lpNorm regularization item or penalty function item；Norm p value range is p ∈ [0,1]；λ is indicated
Regularization parameter；The data length of n expression shock loading vector f；fiIndicate i-th of element in load vector f to be identified；|
|·||2Indicate the l of vector2Norm.
S302: it determines the value of norm p: as p=1, enhancing the convex Optimized model that sparse regularization model is standard；When
P ∈ [0,1) when, it is non-convex for enhancing sparse regularization model.
S303: initialization: regularization parameter λ=0.01 | | HTy||∞~0.5 | | HTy||∞, iteration ends threshold epsilon=10-6, weight adjusting parameter η(0)=0.00001~1, weight matrix is unit matrix W(0)=I, the number of iterations k=0；Wherein, H table
Show transfer function matrix；Y indicates impulse response signal；||·||∞Indicate Infinite Norm；The transposition of subscript T expression vector.
S304: it solves and is based on lpThe sparse regularization model of the enhancing of norm: the l that step S301 is constructedpNorm enhancing is dilute
Thin regularization model is converted to weighting l1Norm Model:
Wherein, min indicates to minimize；H indicates transfer function matrix；F indicates shock loading to be identified；Y indicates that impact is rung
Induction signal；Indicate residual error item；||W(k)f||1Indicate regularization term；||.||1Indicate the l of vector1Norm；λ is indicated just
Then change parameter；W(k)Indicate weight matrix when the number of iterations k；K indicates the number of iterations；Enable intermediate variable x=W(k)F, then wait know
Other load is represented by f=(W(k))-1X, then above formula is converted into the l of standard1Norm regularization model:
Wherein, y indicates impulse response signal；X indicates intermediate variable；λ indicates regularization parameter；Define intermediary matrix A=H
(W(k))-1。
It should be noted that utilizing classical convex optimization method such as interior point method (Interior-Point Method), gradient
Sciagraphy and various iteration thresholds (Iterative Shrinkage/Thresholding) method solve the l of standard1Norm
Regularization model.Preferably, in present case, interior point method is selected to be solved.
S305: weight is updated
K indicates the number of iterations；
For ease of calculation, it can use weight adjusting parameter η(k)=η(0)。
S306: setting iteration weights l1Norm Method Stopping criteria, judges whether iteration restrains according to the following formula:
Wherein, if currently solving f(k+1)Meet above formula Stopping criteria, then terminate iterative process, obtains shock loading f；It is no
Then, the number of iterations k=k+1 is enabled, iterative process return step S303 continues to iterate to calculate, until meeting above formula.
Compared with prior art, it is sparse in shock loading time history to take full advantage of shock pulse for above-described embodiment
Feature, has the advantages that insensitive to measurement noise, regularization parameter, and it is anti-to be able to solve the identification of composite structure shock loading
Problem morbid state problem；It can break through existing based on l2The Tikhonov regularization method time history reconstruct stability of norm is poor
Defect；It is able to ascend based on l1The low limitation of the standardized sparse regularization method peak value accuracy of identification of norm.
In one embodiment, the disclosure also provides a kind of composite structure shock loading identification dress sparse based on enhancing
It sets, comprising:
Excitation vibration module, for obtaining composite structure position excited by impact and responding the biography between point position
Delivery function matrix；
Shock response measurement module is applied to punching caused by the shock loading to be identified of composite structure for measuring
Hit response signal；
Load identification module is based on l for constructingpThe sparse regularization model of the enhancing of norm, and l is weighted using iteration1Model
Counting method, which solves, enhances sparse regularization model, and identification is applied to the shock loading of composite structure.
Implementable solution as one preferred, the impulse response signal are measured by vibrating sensor.
Implementable solution as one preferred, it is described to be based on lpThe sparse regularization model of the enhancing of norm are as follows:
Wherein, H indicates transfer function matrix；F indicates shock loading to be identified；Indicate residual error item,Indicate lpNorm regularization item or penalty function item；Norm p value range is p ∈ [0,1]；λ is indicated just
Then change parameter；The data length of n expression shock loading vector f；fiIndicate i-th of element in load vector f to be identified；||·
||2Indicate the l of vector2Norm.
Implementable solution as one preferred, it is described to be based on lpThe sparse regularization model of the enhancing of norm, which can be exchanged into, to be added
Weigh l1Norm Model:
Wherein, min indicates to minimize；H indicates transfer function matrix；F indicates shock loading to be identified；Y indicates that impact is rung
Induction signal；Indicate residual error item；||w(k)f||1Indicate regularization term；||.||1Indicate the l of vector1Norm；λ is indicated just
Then change parameter；W(k)Indicate weight matrix when the number of iterations k；K indicates the number of iterations；Enable intermediate variable x=W(k)F, then wait know
Other load is represented by f=(W(k))-1X, then above formula is converted into the l1 norm regularization model of standard:
Wherein, y indicates impulse response signal；X indicates intermediate variable；λ indicates regularization parameter；Define intermediary matrix A=H
(W(k))-1。
In one embodiment, if other sides are as shown in Fig. 2, composite laminated plate fixing end is fixed by bolt stem
Free state.The long 400mm of the composite laminated plate, width 400mm, thickness 1mm.Ply stacking-sequence is [0 °/45 °/- 45 °/90 °]s。4
Piece model PCB 333B32 acceleration transducer is mounted on composite laminated plate surface.In the present embodiment, pass through following mistake
Journey is completed to identify the shock loading of the composite material:
1, shock loading is applied to laminate using the impulsive force hammer of model PCB 086C01, repeats to tap position five
It is secondary, while impact force and acceleration signal, the effect of five Secondary Shocks load are recorded by LMS SCADASIII data collection system synchronizing
The frequency response function that point arrives between acceleration measuring point is H1(ω)、H2(ω)、H3(ω)、H4(ω) and H5(ω), by LMS IMPACT mould
It is H (ω) that its average value, which is calculated, in block；
Sample frequency when measuring system frequency response function is 2048Hz, sampling time 1s, data length 2050.Excitation
The conditional number of transfer matrix is up to 1.61E+18 between point and response point.(it should be noted that conditional number is to measure matrix morbid state journey
One index of degree).It is found that laminated composite plate structures shock loading identification indirect problem belongs to Very Ill-conditioned.
2, apply shock loading and measurement acceleration shock response, composite laminated plate is measured using acceleration transducer
Impulse response signal y.Shock loading is applied to laminated composite plate structures using impact force hammer, while by LMS
SCADASIII data collection system records acceleration signal and shock loading signal with the sampling frequency synchronization of 2048Hz.Fig. 3
(a) R under same impact event is shown to Fig. 3 (d)1、R2、R3And R4The acceleration responsive of four different measuring points, it is known that four punchings
It is very fast to hit response attenuation, it is more similar on signal pattern, but there are the differences of amplitude size.Wherein, actual measurement power is believed
Number enhance the comparison other of sparse regularization method as shock loading.
3, it establishes and is based on lpThe sparse regularization model G (f) of the enhancing of norm:
Wherein, H indicates transfer function matrix；F indicates shock loading to be identified；Indicate residual error item,Indicate lpNorm regularization item or penalty function item；Norm p value range is p ∈ [0,1]；λ is indicated just
Then change parameter；The data length of n expression shock loading vector f；fiIndicate i-th of element in load vector f to be identified；||·
||2Indicate the l of vector2Norm.
4, l is weighted using iteration1Norm Method, which solves to enhance, dredges regularization model, and non-convex optimization problem is converted to and is based on
l1The sparse regularization model of norm, specifically includes the following steps:
4.1: initialization: enabling norm p=0.5, regularization parameter λ=0.05 | | HTy||∞, iteration ends threshold epsilon=10-6、
Weight adjusting parameter η(0)=0.0001, weight matrix is unit matrix W(0)=I, the number of iterations k=0；Wherein, H indicates transmitting
Jacobian matrix；Y indicates impulse response signal；||·||∞Indicate Infinite Norm；The transposition of subscript T expression vector.
4.2: solving and be based on lpThe regularization model of norm: by the l in step 3)pNorm enhances sparse regularization model and turns
It is changed to weighting l1Norm Model:
Wherein, min indicates to minimize；H indicates transfer function matrix；F indicates shock loading to be identified；Y indicates that impact is rung
Induction signal；Indicate residual error item；||W(k)f||1Indicate regularization term；||.||1Indicate the l of vector1Norm；λ is indicated just
Then change parameter；W(k)Indicate weight matrix when the number of iterations k；K indicates the number of iterations；Enable intermediate variable x=W(k)F, then wait know
Other load is represented by f=(W(k))-1X, then above formula is converted into the l of standard1Norm regularization model:
Wherein, y indicates impulse response signal；X indicates intermediate variable；λ indicates regularization parameter；Define intermediary matrix A=H
(W(k))-1。
Utilize classical convex optimization method such as interior point method (Interior-Point Method), gradient projection method and each
Kind iteration threshold (Iterative Shrinkage/Thresholding) method solves the l of standard1Norm regularization model.?
In present case, interior point method is selected to be solved.
4.3: updating weight
K indicates the number of iterations；
It can use weight adjusting parameter η for ease of calculation(k)=η(0)。
4.4: setting iteration weights l1Norm Method Stopping criteria, judges whether iteration restrains according to the following formula:
Wherein, if currently solving f(k+1)Meet above formula Stopping criteria, then terminate iterative process, obtains shock loading f；It is no
Then, the number of iterations k=k+1 is enabled, iterative process return step 4.2 continues to iterate to calculate, until meeting above formula.
5, the performance for quantitative assessment difference regularization method in the identification of composite structure shock loading, it is fixed respectively
Adopted time domain overall situation relative error (Relative Error, RE) and shock loading peak value (Peak Relative Error, PRE)
Relative error:
Wherein, fExactAnd fidentifiedIt is that the shock loading of force snesor actual measurement and application regularization method reconstruct respectively
Shock loading.
(a) is to utilize composite structure R to Fig. 4 (d), Fig. 4 (a) to Fig. 4 (d) referring to fig. 41、R2、R3And R4Four are not
With the result of the acceleration signal difference inverting shock loading of measuring point, wherein Fig. 4 (a) indicates measuring point R1；Fig. 4 (b) indicates measuring point
R2；Fig. 4 (c) indicates measuring point R3；Fig. 4 (d) indicates measuring point R4；Concrete outcome is shown in Table 1:
Table 1
As seen from the above table, it is based on lpThe sparse regularization method of the enhancing of norm either shock loading peak value precision still weighs
Structure load history has apparent advantage.Be based on l1The standardized sparse regularization method of norm is compared, and l is based onpNorm
The peak value precision for enhancing sparse regularization method is higher, also more sparse.Be based on l2The Tikhonov method of norm is compared, base
In lpThe sparse regularization method stability of the enhancing of norm is strong, can greatly inhibit to measure noise.
Claims (10)
1. it is a kind of based on sparse composite structure shock loading recognition methods is enhanced, include the following steps:
S100: the transfer function matrix between composite structure position excited by impact and response point position is obtained；
S200: measurement is applied to impulse response signal caused by the shock loading to be identified of composite structure；
S300: l is based on based on step S100 and step S200 constructionpThe sparse regularization model of the enhancing of norm, and added using iteration
Weigh l1Norm Method, which solves, enhances sparse regularization model, and identification is applied to the shock loading of composite structure.
2. step S100 includes the following steps: the method according to claim 1, wherein preferred
S101: the frequency response function H (ω) between composite structure position excited by impact and response point position is obtained；
S102: inverse fast Fourier transform is carried out to the frequency response function H (ω) and obtains unit impulse response function h (t), to list
The discrete acquisition transfer function matrix H of digit pulse receptance function h (t)；Wherein, ω indicates that circular frequency variable, t indicate time variable.
3. according to the method described in claim 2, it is characterized in that, the frequency response function H (ω) passes through hammering in step S101
Method or by establishing composite structure finite element model and carrying out harmonic responding analysis acquisition.
4. the method according to claim 1, wherein the impulse response signal passes through vibration in step S200
Sensor measurement.
5. the method according to claim 1, wherein step S300 includes the following steps:
S301: construction is based on lpThe sparse regularization model G (f) of the enhancing of norm:
Wherein, H indicates transfer function matrix；F indicates shock loading to be identified；Indicate residual error item,Indicate lpNorm regularization item or penalty function item；Norm p value range is p ∈ [0,1]；λ is indicated
Regularization parameter；The data length of n expression shock loading vector f；fiIndicate i-th of element in load vector f to be identified；|
|·||2Indicate the l of vector2Norm；
S302: it determines the value of norm p: as p=1, enhancing the convex Optimized model that sparse regularization model is standard；As p ∈
[0,1) when, it is non-convex for enhancing sparse regularization model；
S303: initialization: regularization parameter λ=0.01 | | HTy||∞~0.5 | | HTy||∞, iteration ends threshold epsilon=10-6, weight
Adjusting parameter η(0)=0.00001~1, weight matrix is unit matrix W(0)=I, the number of iterations k=0；Wherein, H indicates transmitting
Jacobian matrix；Y indicates impulse response signal；||·||∞Indicate Infinite Norm；The transposition of subscript T expression vector；
S304: it solves and is based on lpThe sparse regularization model of the enhancing of norm: the l that step S301 is constructedpNorm enhancing is sparse just
Change model conversion then as weighting l1Norm Model:
Wherein, min indicates to minimize；H indicates transfer function matrix；F indicates shock loading to be identified；Y indicates shock response letter
Number；Indicate residual error item；||W(k)f||1Indicate regularization term；λ indicates regularization parameter；||.||1Indicate the l of vector1
Norm；W(k)Indicate weight matrix when the number of iterations k；K indicates the number of iterations；Enable intermediate variable x=W(k)F, then load to be identified
Lotus is represented by f=(W(k))-1X, then above formula is converted into the l of standard1Norm regularization model:
Wherein, y indicates impulse response signal；X indicates intermediate variable；λ indicates regularization parameter；Define intermediary matrix
A=H (W(k))-1；
S305: weight matrix is updated K indicates the number of iterations；
For ease of calculation, it can use weight adjusting parameter η(k)=η(0)；
S306: setting iteration weights l1Norm Method Stopping criteria, judges whether iteration restrains according to the following formula:
Wherein, if currently solving f(k+1)Meet above formula Stopping criteria, then terminate iterative process, obtains shock loading f；Otherwise,
The number of iterations k=k+1 is enabled, iterative process return step S304 continues to iterate to calculate, until meeting above formula.
6. the method according to claim 1, wherein solving in step S304 and being based on lpThe enhancing of norm is sparse just
Then change model method include it is following any one: convex optimization method such as interior point method, gradient projection method and iteration threshold method.
7. a kind of composite structure shock loading identification device sparse based on enhancing, comprising:
Excitation vibration module, for obtaining composite structure position excited by impact and responding the transmitting letter between point position
Matrix number；
Shock response measurement module is applied to the sound of impact caused by the shock loading to be identified of composite structure for measuring
Induction signal；
Load identification module is based on l for constructingpThe sparse regularization model of the enhancing of norm, and l is weighted using iteration1Norm side
Method, which solves, enhances sparse regularization model, and identification is applied to the shock loading of composite structure.
8. device according to claim 7, which is characterized in that the impulse response signal is measured by vibrating sensor.
9. device according to claim 7, which is characterized in that described to be based on lpThe sparse regularization model of the enhancing of norm are as follows:
Wherein, H indicates transfer function matrix；F indicates shock loading to be identified；Indicate residual error item,Indicate lpNorm regularization item or penalty function item；Norm p value range is p ∈ [0,1]；λ is indicated
Regularization parameter；The data length of n expression shock loading vector f；fiIndicate i-th of element in load vector f to be identified；|
|·||2Indicate the l of vector2Norm.
10. device according to claim 9, which is characterized in that described to be based on lpThe sparse regularization model of the enhancing of norm can
Be converted to weighting l1Norm Model:
Wherein, min indicates to minimize；H indicates transfer function matrix；F indicates shock loading to be identified；Y indicates shock response letter
Number；Indicate residual error item；||W(k)f||1Indicate regularization term；||.||1Indicate the l of vector1Norm；λ indicates regularization
Parameter；W(k)Indicate weight matrix when the number of iterations k；K indicates the number of iterations；Enable intermediate variable x=W(k)F, then load to be identified
Lotus is represented by f=(W(k))-1X, then above formula is converted into the l of standard1Norm regularization model:
Wherein, y indicates impulse response signal；X indicates intermediate variable；λ indicates regularization parameter；Define intermediary matrix A=H (W(k))-1。
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