CN110006526A - A kind of information fusion algorithm of the more weights of multi-measuring point - Google Patents
A kind of information fusion algorithm of the more weights of multi-measuring point Download PDFInfo
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Abstract
The present invention relates to engineering structure vibration test technology fields, more particularly to a kind of information fusion algorithm of the more weights of multi-measuring point, the sensibility that the present invention is mutated system dynamics and nonlinear data using entropy, reasonably distribution weight carries out the information fusion in multiple channels, the vibration measured data of multiple measuring points is pressed, weight distribution is carried out to the importance of analysis result, calculated permutations entropy entropy on this basis, the accuracy of hydro-structure status monitoring, damage diagnosis is significantly improved, to solve the problems, such as the weighted information fusion of multi-measuring point in Engineering Vibration field.
Description
Technical field
The present invention relates to engineering structure vibration test technology fields, and in particular to a kind of information fusion of the more weights of multi-measuring point
Algorithm carries out status monitoring to engineering structure by the algorithm.
Background technique
The many buildings in China include: the structures such as hydraulic structure, bridge, tunnel, existing " rebuilding light pruning "
Problem, Dan Congqi appearance judge with the operating status of first-class building, require far from satisfaction evaluation.Each factor causes
Structure function it is abnormal, necessarily structure vibration signals is caused to change, therefore, vibration measurement is carried out to structure operating status
A kind of important way of monitoring and fault diagnosis.
The research contents of vibration problem is mainly the analysis to vibration measured data, and the effective integration for measured data of shaking is analysis vibration
The key of dynamic problem, decides the validity of status monitoring to a certain extent.In practical projects, each position of engineering structure
Between there are certain couplings, it is therefore, not mutually indepedent between different measuring points, have no to be associated with.The quantity for measured data of shaking
Determine that the abundant degree of signal, the vibration measured data of multiple measuring points more reflect structure self-characteristic relative to single measuring point, but more
A measuring point data analysis, which exists, calculates the drawbacks such as cumbersome, time-consuming is more, if not considering the information fusion problem of multi-measuring point weight,
It may result in the loss of characteristic information, so that there is the problems such as erroneous judgement in the monitoring and judgement for carrying out configuration state, therefore,
The weighted information fusion of multi-measuring point is the key that improve vibration monitoring precision and simplified work.
About the fusion of characteristic information, existing method depends between single measuring point or multiple measuring points mutually solely mostly
It is vertical, independent analysis is carried out to the vibration data of multiple measuring points, the data point that single measuring point is included is very little and does not account for each
The degree of association and weight assignment problem between a measuring point, may lose, multiple surveys in relation to the characteristic information across channel variability
Point vibration data cannot be carried out the fusion of characteristic information by its weight, cause multiple dimensioned arrangement entropy that can generate the estimation of inaccuracy,
As a result the global analysis of structure may be impacted.The letter that multiple channels that are mutually related are distributed by its weight at present
Breath fusion has no specific method.
The concept of entropy is always to measure the important indicator of time series complexity caused by nonlinear dynamic system.More rulers
Degree arrangement entropy is that Aziz etc. is proposed on the basis of arranging entropy, and multiple dimensioned essence is to carry out coarse to original time series
Processing, constructs multiple dimensioned time series, has robustness more better than arrangement entropy, can sensitively capture signal each
Characteristic information under time scale.Since it is more sensitive in terms of detecting nonlinear dynamic system mutation, this method is data point
One of hotspot approach of analysis.
Summary of the invention
It is calculated in view of the deficiencies in the prior art with problem, the information fusion that the present invention provides a kind of more weights of multi-measuring point
Method, the sensibility being mutated using entropy to system dynamics and nonlinear data seek the information fusion of the more weights of multi-measuring point
Method simplifies work, to solve the problems, such as the weighted information fusion of multi-measuring point in Engineering Vibration field.
The present invention solves scheme used by its technical problem: a kind of information fusion algorithm of the more weights of multi-measuring point, packet
Include following steps:
Step 1: in the key position of institute's geodesic structure, laying sensor device, time sequence is surveyed in the vibration for obtaining structure multi-measuring point
Column data { X (i), i=1,2.....N }；
Step 2: extracting the data information of different time sequence length N, and choose suitable scale factor S (generally higher than
10) vibration measured data, is subjected to coarse processing, the time series after obtaining coarse are as follows:
Wherein s indicates scale factor；Expression pairIt is rounded；
Step 3: the Parameters for Phase Space Reconstruction τ of data after each coarse is determined using mutual information method, it is true closely to face method using puppet
The Parameters for Phase Space Reconstruction m of data after fixed each coarse, and phase space reconfiguration is carried out to time series；
Step 4: weight being added to time series, and calculates the probability that each symbol sebolic addressing after having added weight occurs
Step 5: calculating each time series arrangement entropy entropy PE1、PE2、…、PES, obtain the multiple dimensioned of the more weights of multi-measuring point
Arrange entropy MWMPE={ PE1、PE2、…、PES, and number is surveyed as vibration is measured using the mean value of the multiple dimensioned arrangement entropy of the more weights of multi-measuring point
According to the foundation of complexity；Wherein, the mean value of entropy is
Further, the method that the arrangement entropy entropy that coarse sequence is reconfigured in step 4 from step 3 calculates are as follows:
Carrying out phase space reconfiguration to coarse sequence can obtain:
In formula, l indicates first of reconstruct component；τ indicates delay time, and m indicates Embedded dimensions.
With l1, l2..., lmIndicate reconstruct componentThe index of middle each element column, willIt is arranged by ascending order:
If reconstructing in component, there are equal values, sequentially arrange, and calculate the probability of each symbol sebolic addressing appearanceRelative frequencyIt may be defined as:
WhereinL=M- (m-1) τ and j=1 ..., n, n=m！；It is mapped as by the mapping of model space to symbol space；
| | | | it is cardinality of a set；Reflect the distribution of mode in coarse Multivariate Time Series；
For weight,It indicatesArithmetic average, i.e. decagram
The multiple dimensioned arrangement entropy of the more weights of multi-measuring point may be defined as:
Normalization post-processing can obtain:
By original alignment entropy HMWMPEIt is normalized to obtain PE value, after PE value, that is, data coarse processing under each scale
The arrangement entropy entropy of time series, value show that data are more complicated closer to 1, and randomness is bigger；Conversely, showing the complexity of data
It spends smaller with randomness.
Further, the method for Parameters for Phase Space Reconstruction τ being determined using mutual information method in step 3 are as follows:
For time series { X (i)；I=1,2.....n }, take X (i+ τ) to constitute new point range Y (i), when discrete for two
Between sequences correspond respectively to system X, Y, the mutual information according to information theory, between system X, Y are as follows:
I (X, Y)=I (Y, X)=H (X)+H (Y)-H (X, Y)
H (X), H (Y), H (X, Y) respectively indicate the Mutual information entropy between the comentropy and X, Y of system X, Y in formula, specific public
Formula are as follows:
H (X)=- ∑ Px(xi)log2Px(xi)
H (Y)=- ∑ Py(yi)log2Py(yi)
P in formulax(xi)、Py(yi)、Pxy(xi,yj) it is respectively X in xiThe edge distribution probability density in region, Y are in yiRegion
Edge distribution probability density and X, Y in (xi,yj) region joint probability density；
According to above-mentioned formula, the mutual information between X, Y can simplify are as follows:
Can be obtained according to above formula with gradually increasing for delay time T, between X, Y of each τ an association relationship I (X,
Y), show when the value minimum X (i), Y (i) maximum possible it is uncorrelated, when reconstruct, takes mutual information to reach minimum for the first time
τ corresponding to it is as optimum delay time.
Further, closely face the method that method determines Parameters for Phase Space Reconstruction m using pseudo- in step 3 are as follows: Embedded dimensions m compared with
Under small state, each track is overlapped in phase space, forces phase space Central Plains that should fold apart from far point, produces at this time
Raw puppet Neighbor Points；When Embedded dimensions are larger, phase point track is sufficiently spread out in space, and the pseudo- Neighbor Points at former folding are unfolded；If
In dimension m0Place, pseudo- Neighbor Points percentage is down to 0 suddenly, and the percentage no longer changes with the variation of m, then this
When m0As smallest embedding dimension number.
Beneficial effects of the present invention: the present invention is compared with existing entropy calculation method, and first, the obtained information of the present invention
Blending algorithm is to merge multiple channel measuring points by the information that the weight of itself carries out, and the weight of time series is all not identical
, it is individually analyzed relative to traditional entropy to each measuring point, more scientific and be more convenient for observation and the analysis of system.
Second, the present invention is to distribute all measuring points vibration measured data by its weight, and a changes of entropy curve is calculated,
Meanwhile to show more fluctuations, information content abundanter for the entropy curve after information fusion.The method is not losing frequency leakage
The complexity of signal is reduced in the case where frequency, fluctuating range increases, and improves abundant information degree.
Detailed description of the invention
Fig. 1 is the flow chart of the more weight information fusions of multi-measuring point.
Fig. 2 is MPE and MWMPE the analysis comparison diagram of certain concrete gravity dam horizontal direction.
Fig. 3 is MPE and MWMPE the analysis comparison diagram of certain concrete gravity dam vertical direction.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples.
Embodiment 1: Fig. 1 is the flow chart of the more weight information fusions of multi-measuring point.It is roughly divided into figure: data acquisition, entropy
Calculate two parts, wherein entropy calculate in mainly include: coarse, the selection of phase space parameter, the calculating of weight, probability meter
It calculates, arrange the processes such as entropy calculating；The entropy of the more weights of multi-measuring point calculates mainly by obtaining corresponding probability after weighted
Carry out entropy calculating.The present embodiment is merged using the more weight informations of multi-measuring point and determines that the process of changes of entropy is as follows.
(1) sensor dress is laid in the key position of institute's geodesic structure (vibratory output larger or tester position of concern)
It sets and (can be the sensors such as displacement, speed, acceleration, strain), acquisition structure multi-measuring point vibration measured data X (i), i=1,
2.....N}。
(2) data information of different time sequence length N is extracted, and chooses suitable scale factor S (generally higher than 10),
To shake measured data coarse, the time series after obtaining coarse are as follows:
Wherein: s indicates scale factor；Expression pairIt is rounded.
(3) closely face method (False Nearest using mutual information method (Mutual Information, MI) and puppet
Neighbor, FNN) Parameters for Phase Space Reconstruction τ, m of time series after each coarse are determined respectively, and carry out phase space reconfiguration.
The selection of Parameters for Phase Space Reconstruction is the important step before entropy calculates, and is independently determined and combines determining two methods, right
In the detection of unusual condition, it is more accurate to be independently determined method.It seeks Embedded dimensions m here, closely facing method with puppet, asked with mutual information method
Delay time T.
The criterion that the above two parameter is chosen is: when appropriate dimension m is that the pseudo- percentage closely faced a little goes to zero in phase space
Corresponding dimension, and after the dimension, puppet is closely faced percentage and is no longer changed；Optimum delay time τ is to reach most for the first time
Corresponding delay time when small value, m is not less than 1 not less than 2, τ in measured data.
Choose, avoid to Parameters for Phase Space Reconstruction with above method: m value selects unreasonable bring Space Reconstruction
The problems such as homogenization and the dynamics that time series cannot be presented conscientiously are mutated；τ value selects the association of unreasonable bring point
The excessive or too small problem of degree.
(4) probability of each symbol sebolic addressing appearance is calculated(weight is added).
(5) it calculates each time series and arranges entropy entropy PE1、PE2、…、PES, obtain the multiple dimensioned arrangement of the more weights of multi-measuring point
Entropy MWMPE={ PE1、PE2、…、PES, and it is multiple using the mean value of the multiple dimensioned arrangement entropy of the more weights of multi-measuring point as vibration measured data is measured
The foundation of miscellaneous degree.Wherein, the mean value of entropy
Vibration measured data, that is, time series in the present invention, the two are the different addresses of same thing.
The method that the arrangement entropy entropy being reconfigured in step (4) from coarse sequence in step (3) calculates: to coarse sequence
Column, which carry out phase space reconfiguration, to be obtained:
In formula, l indicates first of reconstruct component；τ indicates delay time, and m indicates Embedded dimensions.
With l1, l2..., lmIndicate reconstruct componentThe index of middle each element column, willIt is arranged by ascending order:
If reconstructing in component, there are equal values, sequentially arrange, and calculate the probability of each symbol sebolic addressing appearanceRelative frequencyIt may be defined as:
Wherein: L=M- (d-1) τ and j=1 ..., n, n=m！；It is mapped as by the mapping of model space to symbol space；|
| | | it is cardinality of a set；Reflect the distribution of mode in coarse Multivariate Time Series.
For weight,It indicatesArithmetic average, i.e. decagram
The multiple dimensioned arrangement entropy of the more weights of multi-measuring point may be defined as:
Normalization post-processing can obtain:
For convenience of comparative analysis, by original alignment entropy HMWMPEIt is normalized to obtain PE, PE value is at coarse
After reason under each scale time series arrangement entropy entropy, value shows that data are more complicated closer to 1, and randomness is bigger；Conversely, table
The complexity of bright data and randomness are smaller.
As shown in Figure 2: horizontal direction of certain concrete gravity dam under four different measuring points and four measuring points are fused
Entropy curvilinear motion figure, totally five changes of entropy curves in figure, the data length that 1,2,3,4 measuring points are chosen is N=4096 group,
The entropy number and the fused entropy number of four measuring points that four measuring points individually calculate are 62.As can be seen from the figure: four
Though a different measuring points signal entropy curve cannot be completely overlapped, the variation tendency of entropy and the position of catastrophe point are not much different,
Although the vibration measured data that the sensor that four measuring points are different location exports, not unrelated.Information fusion can basis
Multiple signal fuseds can more be reflected the signal of system true value by certain theory rule at one, have fuse information amount lossless
It loses, the advantages of minute information can be excavated.The vibration measured data of four different measuring points is existed according to the method fusion of the more weights of multi-measuring point
Together, the fluctuation of changes of entropy curve is slightly more compared with four measuring points, and dominant frequency is more prominent after illustrating superposition, reduces whole
Complexity, so that energy is more concentrated, it is bigger that level becomes apparent from regularity.Overall distribution becomes simply, the abundant performance of information content
For increasing for fluctuation.
Fig. 3 chooses same concrete gravity dam in the measuring point of vertical direction, and vertical for the applicability of verification method
Two sensors, vertical direction 5,6 measuring points and the fused changes of entropy curve of two side points is shown and level side are arranged in direction
To identical rule, fluctuate less in the changes of entropy figure curve of No. 5 measuring points, the fluctuation of No. 6 measuring points is slightly more compared with No. 5, passes through two
The changes of entropy curve that the information of side point merges obviously highlights the characteristic of two measuring points, and dominant frequency is more prominent, says
The applicable monitoring and fault diagnosis that vibrational state is carried out in engineering structure vibration field of bright this method.
Under same vibrational state, the complexity for measured data of shaking is identical, therefore data entropy tends to a certain fixed value.Difference is surveyed
The integrity degree of the included information of point is different, so that there are difference for the accuracy of the surveyed entropy of the data information of different measuring points.Usually
In engineering all there is different degrees of coupling in different measuring points, and the information fusion of multiple measuring points enhances the accuracy of its entropy, i.e.,
Multiple measuring points press the entropy of its weight progress information fusion closest to true value, for this purpose, obtaining by the more weight informations of multi-measuring point
This feature of entropy curve carries out the condition monitoring and fault diagnosis of engineering structure.
Before this, arrangement entropy method is mostly used in the fields such as machinery, medicine, engineering structure, side used in this application
Method includes hydro-structure field in each field, has no precedent the information fusion problem of the more weights of multi-measuring point.For this purpose, more overcoming
After scale arranges entropy conventional method, the present invention presses the vibration measured data of multiple measuring points to analysis by the sensitivity of mentioned method
As a result importance carries out weight distribution, on this basis calculated permutations entropy entropy, to improve hydro-structure status monitoring, damage
The accuracy of diagnosis.In addition to water conservancy project field, mentioned method can also develop to bridge, machinery etc. and be related to many necks of signal evaluation
Domain provides the more weight informations of reliable multi-measuring point for signal evaluation and merges thinking, abandons previous multiple measuring points and obtains a plurality of entropy
The method of the observation analysis comparison of curve.
Claims (4)
1. a kind of information fusion algorithm of the more weights of multi-measuring point, includes the following steps:
Step 1: in the key position of institute's geodesic structure, laying sensor device, time series number is surveyed in the vibration for obtaining structure multi-measuring point
According to { X (i), i=1,2.....N }；
Step 2: the data information of different time sequence length N is extracted, and chooses suitable scale factor S (generally higher than 10),
Vibration measured data is subjected to coarse processing, the time series after obtaining coarse are as follows:
Wherein s indicates scale factor；Expression pairIt is rounded；
Step 3: determining the Parameters for Phase Space Reconstruction τ of data after each coarse using mutual information method, determined respectively using pseudo- method of closely facing
The Parameters for Phase Space Reconstruction m of data after coarse, and phase space reconfiguration is carried out to time series；
Step 4: weight being added to time series, and calculates the probability that each symbol sebolic addressing after having added weight occurs
Step 5: calculating each time series arrangement entropy entropy PE1、PE2、…、PES, obtain the multiple dimensioned arrangement of the more weights of multi-measuring point
Entropy MWMPE={ PE1、PE2、…、PES, and it is multiple using the mean value of the multiple dimensioned arrangement entropy of the more weights of multi-measuring point as vibration measured data is measured
The foundation of miscellaneous degree；Wherein, the mean value of entropy is
2. the more weight information fusion methods of multi-measuring point according to claim 1, it is characterised in that: from coarse sequence in step 3
The method that the arrangement entropy entropy that column are reconfigured in step 4 calculates are as follows:
Carrying out phase space reconfiguration to coarse sequence can obtain:
In formula, l indicates first of reconstruct component；τ indicates delay time, and m indicates Embedded dimensions.
With l1, l2..., lmIndicate reconstruct component Yl m,τ,sThe index of middle each element column, by Yl m,τ,sIt is arranged by ascending order:
If reconstructing in component, there are equal values, sequentially arrange, and calculate the probability of each symbol sebolic addressing appearancePhase
To frequencyIt may be defined as:
WhereinL=M- (m-1) τ and j=1 ..., n, n=m！；It is mapped as by the mapping of model space to symbol space；||·
| | it is cardinality of a set；Reflect the distribution of mode in coarse Multivariate Time Series；
For weight,Indicate Yl m,τ,sArithmetic average, i.e. decagram
The multiple dimensioned arrangement entropy of the more weights of multi-measuring point may be defined as:
Normalization post-processing can obtain:
By original alignment entropy HMWMPEIt is normalized to obtain PE value, each scale lower time after PE value, that is, data coarse processing
The arrangement entropy entropy of sequence, value show that data are more complicated closer to 1, and randomness is bigger；Conversely, show the complexities of data with
Randomness is smaller.
3. the more weight information fusion methods of multi-measuring point according to claim 1, it is characterised in that: use mutual information in step 3
The method that method determines Parameters for Phase Space Reconstruction τ are as follows:
For time series { X (i)；I=1,2.....n }, take X (i+ τ) to constitute new point range Y (i), for two discrete time sequences
Both column correspond respectively to system X, Y, the mutual information according to information theory, between system X, Y are as follows:
I (X, Y)=I (Y, X)=H (X)+H (Y)-H (X, Y)
H (X), H (Y), H (X, Y) respectively indicate the Mutual information entropy between the comentropy and X, Y of system X, Y, specific formula in formula
Are as follows:
H (X)=- ∑ Px(xi)log2Px(xi)
H (Y)=- ∑ Py(yi)log2Py(yi)
P in formulax(xi)、Py(yi)、Pxy(xi,yj) it is respectively X in xiThe edge distribution probability density in region, Y are in yiThe edge in region
Distribution probability density and X, Y are in (xi,yj) region joint probability density；
According to above-mentioned formula, the mutual information between X, Y can simplify are as follows:
With gradually increasing for delay time T, an association relationship I (X, Y) can be obtained between X, Y of each τ according to above formula, when
Show uncorrelated, its institute when reconstruct takes mutual information to reach minimum for the first time of X (i), Y (i) maximum possible when the value minimum
Corresponding τ is as optimum delay time.
4. the more weight information fusion methods of multi-measuring point according to claim 1, it is characterised in that: closely faced in step 3 using puppet
The method that method determines Parameters for Phase Space Reconstruction m are as follows: Embedded dimensions m is under smaller state, and each track is overlapped in phase space, compels
It fold phase space Central Plains should apart from far point, generate pseudo- Neighbor Points at this time；When Embedded dimensions are larger, phase in space
Point track is sufficiently spread out, and the pseudo- Neighbor Points at former folding are unfolded；If in dimension m0Place, pseudo- Neighbor Points percentage drop suddenly
To 0, and the percentage no longer changes with the variation of m, then m at this time0As smallest embedding dimension number.
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