CN104914167B - Acoustic emission source locating method based on sequential Monte Carlo algorithm - Google Patents
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Abstract
The invention discloses a kind of localization method based on sequential Monte Carlo algorithm, first to the acoustic emission source in isotropism planar structure, arrange that multiple (being no less than 4) are used for the sensor for receiving the acoustic emission signal that acoustic emission source is sent；Then acoustic emission source positioning is changed into Bayesian filter problem, system equation and measurement equation is set up respectively, using the ripple between each sensor and master reference up to the moment as known measurement amount, unknown state variable is used as using source position and velocity of wave.Due to lacking analytic solutions, estimation is iterated to unknown state variable using sequential Monte Carlo simulation algorithm, so as to realize the positioning of acoustic emission source under the influence of uncertain factor.
Description
Technical field
The invention belongs to engineering structure technical field of nondestructive testing, there is provided a kind of sound based on sequential Monte Carlo algorithm
Emitting source positioning method.
Background technology
Acoustic emission is that the transient stress wave that produces is judged in structure when germinating is damaged according to inside configuration and being extended
A kind of monitoring method of portion's damage.Traditional acoustic emission from acoustic emission signal by obtaining acoustic emission parameters come to damage
It is estimated, each acoustic emission parameters is the specific descriptions to sound emission process.With the development of guided wave theory, in recent years
Come, acoustic emission starts to Modal Acoustic Emission to change from parameter type sound emission.Modal Acoustic Emission is to the sound emission in material
Ultrasound mode where source is studied, and what it was analyzed is the ultrasound mode ripple that acoustic emission source is produced.By acoustic emission signal
After the supersonic guide-wave for resolving into different mode, it is possible to use to the understanding of supersonic guide-wave, feature and property to acoustic emission source are entered
Row analysis infers that the location technology of wherein acoustic emission source is always a major issue of sound emission research field.
At present in Acoustic Emission location field, and many localization methods are proposed, wherein reaching the positioning side at moment based on ripple
Method is widely used.By the speed of known sensing station, and supersonic guide-wave, it can be determined using different trigonometric ratios
Position method, such as solves one group of nonlinear equation, or uses optimized algorithm iterative, obtains the position of acoustic emission source.At these
In method, the extraction to different mode guided wave due in acoustic emission signal is extremely important, directly affects acoustic emission source positioning
Precision.But due to a variety of causes such as signal noise, frequency dispersion effect, signal transactings, it is difficult to obtain accurate earthwave up to time data.
Meanwhile, in order to which the velocity of wave of different mode guided wave is determined in advance, it is necessary to according to the material parameter of structure, calculate and obtain by guided wave equation
.But due to reasons such as manufacturing process, material agings, the actual material parameter of structure has certain difference with nominal value, simultaneously
Due to factors such as model simplification, temperature effects, the velocity of wave of theoretical calculation also has certain error with actual velocity of wave.These problems
The influence come to acoustic emission source positioning belt is all probabilistic.Traditional location algorithm all being to determine property that the moment is reached based on ripple
Method, do not account for the influence of these uncertain factors.
The content of the invention
The purpose of the present invention is to be in acoustic emission source positioning, it is considered to uncertain caused by model error and measurement noise
A kind of influence, it is proposed that localization method based on sequential Monte Carlo algorithm, the acoustic emission source position available for two-dimension plane structure
Put the joint solution with velocity of wave.
To realize the technical purpose of the above, the present invention will take following technical scheme：
A kind of acoustic emission source locating method based on sequential Monte Carlo algorithm, sends out for sound under the influence of uncertain factor
Penetrate the positioning in source；Comprise the following steps：
(1) for the acoustic emission source in isotropism planar structure, arrange N number of for receiving what acoustic emission source was sent
The sensor of acoustic emission signal；In described each sensor, one of those is set to master reference, remaining is then set to time biography
Sensor；Plane coordinate system is set up in foregoing isotropism planar structure so that the coordinate of acoustic emission source is (xs,ys), it is main to pass
The coordinate of sensor is (xi,yi), the coordinate of each sensor is (xj, yj) (j=1 ... i-1, i+1...N), wherein, N is just whole
Number (number of probes, N >=4)；
(2) due in that each sensor receives acoustic emission signal is obtained by signal processing method, and calculates each
The poor Δ t of the individual due between sensor and master referenceij, form measurement vector Z；
(3) position of acoustic emission source and corresponding acoustic emission signal velocity of wave are characterized with state vector X；Definition status vector
X=[xs,ys,Vg]T, wherein：(xs,ys) be acoustic emission source position coordinates, VgFor the velocity of wave of corresponding acoustic emission signal；
(4) Acoustic Emission location problem is converted into Bayesian filter problem, sets up system equation and measurement equation is changed
For solving state vector X；Wherein：
System equation expression formula is
Xk=Xk-1+ωk-1
Measuring equation expression formula is
Zk=h (Xk)+υk
In formula：K is number of iterations；ω is system noise；υ is measurement noise；H is a nonlinear function, is surveyed for expressing
Measure the element Δ t in vector ZijRelation between the element in state vector X；In addition, when setting up system equation, it is assumed that be
System noise ω average is zero, and its covariance matrix isNormal distribution；When setting up measurement equation, it is assumed that measurement is made an uproar
Sound υ average is zero, and its covariance matrix isNormal distribution；
In formula：ΔtijIt is meant that the due between No. j-th sensor and master reference (i-th of sensor) is poor.
(5) numerical solution is carried out using sequential Monte Carlo analogy method, iteration calculates state vector X estimates, and
The state vector X estimates gone out using meeting the last time iteration of iterations are used as acoustic emission source position (xs,ys) and it is corresponding
Velocity of wave VgDiscre value.
Technical scheme more than, relative to prior art, the present invention has the following advantages that：
The present invention to acoustic emission source when positioning, it is contemplated that uncertain shadow caused by model error and measurement noise
Ring, acoustic emission source positioning is changed into Bayesian filter problem by this method, system equation and measurement equation are set up respectively, with each
Ripple between secondary sensor and master reference, as known measurement amount, is become up to the moment using source position and velocity of wave as location status
Amount.Due to lacking analytic solutions, estimation is iterated to unknown state variable using sequential Monte Carlo simulation algorithm, it is final to realize
The positioning of acoustic emission source under the influence of uncertain factor.
Brief description of the drawings
Fig. 1 is the acoustic emission source positioning schematic diagram up to the moment based on ripple；
Fig. 2 is detection example schematic diagram；
Fig. 3 is piezoelectric transducer S1The sound emission simulation source P received1The acoustic emission signal sent；
Fig. 4 is piezoelectric transducer S3The sound emission simulation source P received1The acoustic emission signal sent；
Fig. 5 is the sound emission simulation source P of sequential Monte Carlo algorithm estimation1The x coordinate of position；
Fig. 6 is the sound emission simulation source P of sequential Monte Carlo algorithm estimation1The y-coordinate of position；
Fig. 7 is the velocity of wave V of 40kHz guided wave compositions in the acoustic emission signal that sequential Monte Carlo algorithm is estimatedg。
Embodiment
With reference to the accompanying drawings and detailed description, the present invention is furture elucidated.It should be understood that following embodiments are only
For illustrating the present invention rather than limitation the scope of the present invention.It should be noted that these accompanying drawings are simplified schematic diagram,
Only illustrate the basic structure of the present invention in a schematic way, therefore it only shows the composition relevant with the present invention.
Acoustic emission source locating method of the present invention based on sequential Monte Carlo algorithm, for uncertain factor shadow
The positioning of lower acoustic emission source is rung, the localization method, which is mainly, considers uncertain shadow caused by model error and measurement noise
Ring；Comprise the following steps：
(1) for the acoustic emission source in isotropism planar structure, arrange N number of for receiving what acoustic emission source was sent
The sensor (reference picture 1) of acoustic emission signal；In described each sensor, one of those is set to master reference, remaining
Then it is set to time sensor；Plane coordinate system is set up in foregoing isotropism planar structure so that the coordinate of acoustic emission source is
(xs,ys), the coordinate of master reference is (xi,yi), the coordinate of each sensor is (xj, yj) (j=1 ... i-1, i+1...N), its
In, N is positive integer (number of probes, N >=4)；
(2) due in that each sensor receives acoustic emission signal is obtained by signal processing method, and calculates each
The poor Δ t of due between secondary sensor and master referenceij, form measurement vector Z；
(3) position of acoustic emission source and corresponding acoustic emission signal velocity of wave are characterized with state vector X；Definition status vector
X=[xs,ys,Vg]T, wherein：(xs,ys) be acoustic emission source position coordinates, VgFor the velocity of wave of correspondence acoustic emission signal；
(4) Acoustic Emission location problem is converted into Bayesian filter problem, sets up system equation and measurement equation is changed
For solving state vector X；Wherein：
System equation expression formula is
Xk=Xk-1+ωk-1
Measuring equation expression formula is
Zk=h (Xk)+υk
In formula：K is number of iterations；ω is system noise；υ is measurement noise；H is a nonlinear function, is surveyed for expressing
Measure the relation between vector Z and state vector X；
In formula：ΔtijIt is meant that the due between No. j-th sensor and master reference (i-th of sensor) is poor.
(5) numerical solution is carried out using sequential Monte Carlo analogy method, iteration calculates state vector X estimates, and
The state vector X estimates gone out using meeting the last time iteration of iterations are used as acoustic emission source position (xs,ys) and it is corresponding
Velocity of wave VgDiscre value.
What is referred in above-mentioned steps carries out numerical solution estimated state vector X's using sequential Monte Carlo analogy method
Comprise the concrete steps that：
(i) k=0, initializes NpIndividual particle：Rule of thumb (planar structure size and material character) determines prior distribution p
(X0), and obtained from prior distribution random samplingAnd set weightI=1 ..., Np；
(ii) k=k+1, the random generation process noise from system noise ω distributionAnd using system equation prediction
(iii) calculateUsing measurement equation estimationUpdate weightAnd
Normalized weight
(iv) effective sample quantity N is calculatedeffIf, NeffLess than Np/ 2, start resampling program, with probabilityGenerate one group of new particle
(v) X is calculatedkAverageIt is used as XkEstimate；
(vi) step (ii) is returned to, untill number of iterations set in advance is reached or the condition of convergence is met, output is last
Generation state vector X estimates are used as final state vector X discre values.
Example is described
As shown in Fig. 2 the structure monitored is each aluminium sheet to the uniform same sex, thickness is 2mm, in a 300mm thereon
Piezoelectric element of 6 a diameter of 10mm thickness for 1mm is arranged as sensor in × 400mm region, and the material of sensor is
P51, is respectively designated as S1-S6, each sensor coordinates are as shown in table 1.
The generation of sound emission is simulated by the way of the lead that breaks on aluminium sheet, totally 5 sound emission simulation sources, respectively P1-P5,
A sound emission simulation source is only produced every time, and the position of each sound emission simulation source is as shown in table 2.
With S3For master reference, other sensors are time sensor.When the magnitude of voltage that master reference is monitored exceedes necessarily
During threshold value, it is believed that acoustie emission event occurs, all the sensors start simultaneously at collection acoustic emission signal.If Fig. 3 and Fig. 4 is pressure respectively
Electric transducer S1And S3The sound emission simulation source P received1The acoustic emission signal sent.Acoustic emission signal is collected in sensor
Afterwards, acoustic emission signal is handled using Morlet wavelet transformations, the ripple for extracting each signal reaches the moment, and calculates time sensing
Ripple between device and master reference reaches time difference, and table 3 is shown the ripple extracted using wavelet transformation in 40kHz frequencies and reaches the moment
Difference data.
With sound emission simulation source P1Exemplified by, set up measurement vector Z=[31.1 64.4 34.4 66.9 5.1]T, using institute
The vector X=of the localization method identification state based on the sequential Monte Carlo algorithm [x of inventions,ys,Vg]T, Fig. 5 is sequential Meng Teka
The sound emission simulation source P of Lip river algorithm identification1The x coordinate of position, Fig. 6 is the sound emission simulation source of sequential Monte Carlo algorithm identification
P1The y-coordinate of position, Fig. 7 is the velocity of wave V of 40kHz guided wave compositions in the acoustic emission signal that sequential Monte Carlo algorithm is recognizedg.Table 4
For the positioning result of each sound emission simulation source.
Each sensor coordinates (mm) of table 1
| Sensor mark | S1 | S2 | S3 | S4 | S5 | S6 |
| X coordinate | 0 | 150 | 150 | 0 | -150 | -150 |
| Y-coordinate | -200 | -200 | 0 | 200 | 200 | 0 |
Each sound emission simulation source coordinate (mm) of table 2
| Simulation source mark | P1 | P2 | P3 | P4 | P5 |
| X coordinate | 0 | 100 | 50 | -100 | -100 |
| Y-coordinate | 0 | -50 | 100 | 100 | -150 |
The ripple of table 3 reaches moment difference data (μ s)
| Δt13 | Δt23 | Δt43 | Δt53 | Δt63 | |
| P1 | 31.1 | 64.4 | 34.4 | 66.9 | 5.1 |
| P2 | 71.7 | 54.5 | 125.9 | 178.0 | 120.5 |
| P3 | 99.7 | 105.2 | -18.5 | 52.7 | 52.4 |
| P4 | 32.0 | 80.9 | -83.5 | -104.4 | -108.5 |
| P5 | -112.4 | -23.7 | 46.5 | 36.7 | -82.4 |
Each sound emission simulation source positioning result (mm) of table 4
| Simulation source mark | P1 | P2 | P3 | P4 | P5 |
| X coordinate | 4.2 | 108.1 | 54.2 | -98.6 | -102.5 |
| Y-coordinate | -2.4 | -50.2 | 103.7 | 97.2 | -153.9 |
Technological means disclosed in the present invention program is not limited only to the technological means disclosed in above-mentioned technological means, in addition to
Constituted technical scheme is combined by above technical characteristic.
Using the above-mentioned desirable embodiment according to the present invention as enlightenment, by above-mentioned description, relevant staff is complete
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property scope is not limited to the content on specification, it is necessary to its technical scope is determined according to right.
Claims (5)
1. a kind of acoustic emission source locating method based on sequential Monte Carlo algorithm, for sound emission under the influence of uncertain factor
The positioning in source；It is characterised in that it includes following steps：
(1) for the acoustic emission source in isotropism planar structure, arrange N number of for receiving the sound hair that acoustic emission source is sent
Penetrate the sensor of signal；In described each sensor, one of those is set to master reference, remaining is then set to time sensing
Device；Plane coordinate system is set up in foregoing isotropism planar structure so that the coordinate of acoustic emission source is (xs,ys), main sensing
The coordinate of device is (xi,yi), the coordinate of each sensor is (xj, yj) (j=1 ... i-1, i+1...N), wherein, N is positive integer,
N≥4；
(2) due in for the acoustic emission signal that each sensor is received is obtained by signal processing method, and calculates each
The poor Δ t of due between secondary sensor and master referenceij, form measurement vector Z；
(3) position of acoustic emission source and corresponding acoustic emission signal velocity of wave are characterized with state vector X；Definition status vector X=
[xs,ys,Vg]T, wherein：(xs,ys) be acoustic emission source position coordinates, VgFor the velocity of wave of correspondence acoustic emission signal；
(4) Acoustic Emission location problem is converted into Bayesian filter problem, sets up system equation and measurement equation is iterated and asked
Solve state vector X；Wherein：
System equation expression formula is
Xk=Xk-1+ωk-1
Measuring equation expression formula is
Zk=h (Xk)+υk
In formula：K is number of iterations；ω is system noise；υ is measurement noise；H is a nonlinear function, for express measurement to
Measure the element Δ t in ZijRelation between the element in state vector X；
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In formula：ΔtijIt is poor for the due between No. j-th sensor and i-th of sensor；
(5) numerical solution is carried out using sequential Monte Carlo analogy method, iteration calculates state vector X estimates, and with full
The state vector X estimates that the last time iteration of sufficient iterations goes out are used as acoustic emission source position (xs,ys) and corresponding velocity of wave
VgDiscre value；
What is referred in step (5) carries out the specific of numerical solution estimated state vector X using sequential Monte Carlo analogy method
Step is：
(i) k=0, initializes NpIndividual particle：Prior distribution p (X are determined according to planar structure size and material character0), and from elder generation
Test distribution p (X0) random sampling acquisitionAnd set weightI=1 ..., Np；
(ii) k=k+1, the random generation process noise from system noise ω distributionAnd using system equation prediction
(iii) calculateUsing measurement equation estimationUpdate weightAnd normalize
Weight
(iv) effective sample quantity N is calculatedeffIf, NeffLess than Np/ 2, start resampling program, with probabilityGenerate one group of new particle
(v) X is calculatedkAverageIt is used as XkEstimate；
(vi) step (ii) is returned to, untill number of iterations set in advance is reached or the condition of convergence is met, last generation is exported
State vector X estimates are used as final state vector X discre values.
2. the acoustic emission source locating method according to claim 1 based on sequential Monte Carlo algorithm, it is characterised in that：Institute
The signal processing method stated is small wave converting method.
3. the acoustic emission source locating method according to claim 1 based on sequential Monte Carlo algorithm, it is characterised in that：Institute
The iterations stated is determined by presetting, or by the condition of convergence；The condition of convergence is：The estimate of continuous multiple iteration steps it
Difference is less than predetermined value.
4. the acoustic emission source locating method according to claim 1 based on sequential Monte Carlo algorithm, it is characterised in that：
When setting up system equation, it is assumed that system noise ω average is zero, and its covariance matrix isNormal distribution；Surveyed setting up
When measuring equation, it is assumed that measurement noise υ average is zero, and its covariance matrix isNormal distribution.
5. the acoustic emission source locating method according to claim 1 based on sequential Monte Carlo algorithm, it is characterised in that：Sound
The uncertain factor of emission source is model error and measurement noise.
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| CN106199521B (en) * | 2016-07-19 | 2017-06-06 | 中南大学 | A kind of abnormal then data identification method of microseism or sound emission based on minimum range |
| CN106526074B (en) * | 2016-09-23 | 2018-09-28 | 天津大学 | The airborne three-dimensional smell source direction detection method of rotor wing unmanned aerial vehicle |
| CN110376290B (en) * | 2019-07-19 | 2020-08-04 | 中南大学 | Acoustic emission source positioning method based on multi-dimensional nuclear density estimation |
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| DE102007043649B4 (en) * | 2007-09-13 | 2012-03-22 | Siemens Ag | Method for increasing the location accuracy of unsynchronized radio subscribers |
| CN101806882A (en) * | 2010-03-11 | 2010-08-18 | 沈阳化工学院 | Locating method of emitting source |
| CN102129063B (en) * | 2010-12-23 | 2012-10-10 | 中南大学 | Method for positioning micro seismic source or acoustic emission source |
| CN102435980B (en) * | 2011-09-15 | 2013-05-08 | 中南大学 | Analytical solution-based acoustic emission source or micro seismic source positioning method |
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Inventor after: Yan Gang Inventor after: Tang Jianfei Inventor after: Cai Chenning Inventor before: Yan Gang Inventor before: Tang Jianfei Inventor before: Cai Chenning Inventor before: Jin Zhiguang Inventor before: Zhang Lin Inventor before: Li Yongzhou |
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