CN104914167A - SMC (Sequential Monte Carlo) algorithm based acoustic emission source location method - Google Patents

SMC (Sequential Monte Carlo) algorithm based acoustic emission source location method Download PDF

Info

Publication number
CN104914167A
CN104914167A CN201510338750.4A CN201510338750A CN104914167A CN 104914167 A CN104914167 A CN 104914167A CN 201510338750 A CN201510338750 A CN 201510338750A CN 104914167 A CN104914167 A CN 104914167A
Authority
CN
China
Prior art keywords
acoustic emission
emission source
sensor
monte carlo
sequential monte
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201510338750.4A
Other languages
Chinese (zh)
Other versions
CN104914167B (en
Inventor
严刚
汤剑飞
蔡晨宁
金志光
张�林
李永洲
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN201510338750.4A priority Critical patent/CN104914167B/en
Publication of CN104914167A publication Critical patent/CN104914167A/en
Application granted granted Critical
Publication of CN104914167B publication Critical patent/CN104914167B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Landscapes

  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)

Abstract

The invention discloses an SMC (Sequential Monte Carlo) algorithm based location method. The method comprises the following steps: distributing not less than four sensors for receiving acoustic emission signals emitted by an acoustic emission source on acoustic emission sources of an isotropic planar structure; converting acoustic emission source location into bayesian filtering, building a system equation and a measurement equation respectively, using Poda moment between each sub-sensor and a main sensor as a known measurement quantity and taking a source position and the wave velocity as unknown state variables. The SMC simulation algorithm is adopted to perform iterative estimation on unknown status variables due to lack of analytical solution, and an acoustic emission source is located under the influence of uncertain factors.

Description

Based on the acoustic emission source locating method of sequential Monte Carlo algorithm
Technical field
The invention belongs to engineering structure technical field of nondestructive testing, provide a kind of acoustic emission source locating method based on sequential Monte Carlo algorithm.
Background technology
Acoustic emission judges according to the transient stress wave produced during inside configuration damage initiation and propogation a kind of monitoring method that inside configuration is damaged.Traditional acoustic emission is assessed damage by obtaining acoustic emission parameters from acoustic emission signal, and each acoustic emission parameters is the specific descriptions to sound emission process.Along with the development of guided wave theory, in the last few years, acoustic emission started to change from parameter type acoustic emission to Modal Acoustic Emission.Modal Acoustic Emission studies the ultrasound mode at the acoustic emission source place in material, and what it was analyzed is the ultrasound mode ripple that acoustic emission source produces.After supersonic guide-wave acoustic emission signal being resolved into different mode, can utilize the understanding to supersonic guide-wave, carry out analysis to the characteristic sum character of acoustic emission source and infer, wherein the location technology of acoustic emission source is a major issue of acoustic emission research field always.
At present in Acoustic Emission location field, and propose many localization methods, wherein reach the localization method in moment based on ripple and be widely used.By known sensing station, and the speed of supersonic guide-wave, different trigonometric ratio localization methods can be adopted, as solved one group of nonlinear equation, or using optimized algorithm iterative, obtaining the position of acoustic emission source.In these methods, extremely important to the extraction of different mode guided wave due in acoustic emission signal, directly affect the precision of acoustic emission source location.But due to a variety of causes such as signal noise, frequency dispersion effect, signal transacting, be difficult to obtain accurate earthwave and reach time data.Meanwhile, in order to determine the velocity of wave of different mode guided wave in advance, needing the material parameter according to structure, being calculated by guided wave equation and obtaining.But due to the reason such as manufacturing process, material aging, the material parameter of structure reality and nominal value have certain difference, simultaneously due to the factor such as model simplification, temperature effect, the velocity of wave of theory calculate also has certain error with the velocity of wave of reality.It is all probabilistic that these problems locate the impact brought to acoustic emission source.Traditional to reach the location algorithm in moment based on ripple be all deterministic method, do not consider the impact of these uncertain factors.
Summary of the invention
Namely object of the present invention is in acoustic emission source location, consider the uncertainty impact that model error and measurement noises cause, propose a kind of localization method based on sequential Monte Carlo algorithm, can be used for solving acoustic emission source position and combining of velocity of wave of two-dimension plane structure.
For realizing above technical purpose, the present invention will take following technical scheme:
Based on an acoustic emission source locating method for sequential Monte Carlo algorithm, for the location of the lower acoustic emission source of uncertain factor impact; Comprise the following steps:
(1) for the acoustic emission source be in isotropy planar structure, N number of sensor for receiving the acoustic emission signal that acoustic emission source sends is arranged; In described each sensor, one of them is decided to be master reference, remaining is then decided to be time sensor; Aforesaid isotropy planar structure sets up plane coordinate system, makes the coordinate of acoustic emission source be (x s, y s), the coordinate of master reference is (x i, y i), the coordinate of each sensor is (x j, y j) (j=1 ... i-1, i+1...N), wherein, N is positive integer (number of probes, N>=4);
(2) obtain by signal processing method the due in that each sensor receives acoustic emission signal, and calculate the due in difference Δ t between each sensor and master reference ij, formed and measure vector Z;
(3) position of acoustic emission source and corresponding acoustic emission signal velocity of wave is characterized with state vector X; Definition status vector X=[x s, y s, V g] t, wherein: (x s, y s) be the position coordinates of acoustic emission source, V gfor the velocity of wave of the acoustic emission signal of correspondence;
(4) Acoustic Emission location problem is converted into Bayesian filter problem, sets up system equation and measure equation and carry out iterative state vector X; Wherein:
System equation expression formula is
X k＝X k-1+ω k-1
Measuring equation expression formula is
Z k＝h(X k)+υ k
In formula: k is number of iterations; ω is system noise; υ is measurement noises; H is a nonlinear function, for expressing the element Δ t measured in vector Z ijand the relation between the element in state vector X; In addition, when setting up system equation, the average of supposing the system noise ω is zero, and its covariance matrix is normal distribution; When setting up measurement equation, suppose that the average of measurement noises υ is zero, and its covariance matrix is normal distribution;
Δt i j = ( x s - x i ) 2 + ( y s - y i ) 2 - ( x s - x j ) 2 + ( y s - y j ) 2 V g
In formula: Δ t ijimplication is that a jth secondary due between sensor and master reference (i-th sensor) is poor.
(5) adopt sequential Monte Carlo analogy method to carry out numerical solution, iteration calculates state vector X estimated value, and the state vector X estimated value gone out using the last iteration meeting iterations is as acoustic emission source position (x s, y s) and corresponding velocity of wave V gdiscre value.
According to above technical scheme, relative to prior art, the present invention has following advantage:
The present invention is when positioning acoustic emission source, consider the uncertainty impact that model error and measurement noises cause, acoustic emission source location is changed into Bayesian filter problem by this method, set up system equation respectively and measure equation, the moment is reached as known measuring amount, using source position and velocity of wave as location status variable using the ripple between each sensor and master reference.Owing to lacking analytic solution, adopt sequential Monte Carlo modeling algorithm to carry out iterative estimate to unknown state variable, finally realize the location of the lower acoustic emission source of uncertain factor impact.
Accompanying drawing explanation
Fig. 1 is the acoustic emission source location schematic diagram reaching the moment based on ripple;
Fig. 2 is detection example schematic diagram;
Fig. 3 is piezoelectric sensor S 1the acoustic emission dummy source P received 1the acoustic emission signal sent;
Fig. 4 is piezoelectric sensor S 3the acoustic emission dummy source P received 1the acoustic emission signal sent;
Fig. 5 is the acoustic emission dummy source P that sequential Monte Carlo algorithm is estimated 1the x coordinate of position;
Fig. 6 is the acoustic emission dummy source P that sequential Monte Carlo algorithm is estimated 1the y coordinate of position;
Fig. 7 is the velocity of wave V of 40kHz guided wave composition in the acoustic emission signal of sequential Monte Carlo algorithm estimation g.
Embodiment
Below in conjunction with the drawings and specific embodiments, illustrate the present invention further.Following embodiment should be understood only be not used in for illustration of the present invention and limit the scope of the invention.It should be noted that, these accompanying drawings are the schematic diagram of simplification, only basic structure of the present invention are described in a schematic way, and therefore it only shows the formation relevant with the present invention.
Acoustic emission source locating method based on sequential Monte Carlo algorithm of the present invention, for the location of the lower acoustic emission source of uncertain factor impact, this localization method mainly considers the uncertainty impact that model error and measurement noises cause; Comprise the following steps:
(1) for the acoustic emission source be in isotropy planar structure, N number of sensor (with reference to Fig. 1) for receiving the acoustic emission signal that acoustic emission source sends is arranged; In described each sensor, one of them is decided to be master reference, remaining is then decided to be time sensor; Aforesaid isotropy planar structure sets up plane coordinate system, makes the coordinate of acoustic emission source be (x s, y s), the coordinate of master reference is (x i, y i), the coordinate of each sensor is (x j, y j) (j=1 ... i-1, i+1...N), wherein, N is positive integer (number of probes, N>=4);
(2) obtain by signal processing method the due in that each sensor receives acoustic emission signal, and calculate each the due in difference Δ t between sensor and master reference ij, formed and measure vector Z;
(3) position of acoustic emission source and corresponding acoustic emission signal velocity of wave is characterized with state vector X; Definition status vector X=[x s, y s, V g] t, wherein: (x s, y s) be the position coordinates of acoustic emission source, V gfor 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 measure equation and carry out iterative state vector X; Wherein:
System equation expression formula is
X k＝X k-1+ω k-1
Measuring equation expression formula is
Z k＝h(X k)+υ k
In formula: k is number of iterations; ω is system noise; υ is measurement noises; H is a nonlinear function, for expressing the relation measured between vector Z and state vector X;
Δt i j = ( x s - x i ) 2 + ( y s - y i ) 2 - ( x s - x j ) 2 + ( y s - y j ) 2 V g
In formula: Δ t ijimplication is that a jth secondary due between sensor and master reference (i-th sensor) is poor.
(5) adopt sequential Monte Carlo analogy method to carry out numerical solution, iteration calculates state vector X estimated value, and the state vector X estimated value gone out using the last iteration meeting iterations is as acoustic emission source position (x s, y s) and corresponding velocity of wave V gdiscre value.
The employing sequential Monte Carlo analogy method mentioned in above-mentioned steps to carry out the concrete steps of numerical solution estimated state vector X is:
(i) k=0, initialization N pindividual particle: rule of thumb (planar structure size and material character) determines prior distribution p (X 0), and obtain from prior distribution random sampling and establish weight i=1 ..., N p;
(ii) k=k+1, random generation process noise from the distribution of system noise ω and adopt system equation to predict
(iii) calculate adopt and measure equation estimation upgrade weight w k i = w k - 1 i p ( Z k | X k i ) , And normalized weight w k i = w k i / Σ i = 1 N p w k i ;
(iv) effective sample quantity N is calculated effif, N effbe less than N p/ 2, start resampling program, with probability p ( X k i * = X k i ) = w k i , Generate one group of new particle p ( X k i * = X k i ) = w k i ;
V () calculates X kaverage as X kestimated value;
(vi) get back to step (ii), until the number of iterations that presets arrives or the condition of convergence meets, export last generation state vector X estimated value as final state vector X discre value.
Example describes
As shown in Figure 2, the structure of monitoring is each aluminium sheet to the even same sex, and thickness is 2mm, and 6 diameters are arranged to be 10mm thickness be that the piezoelectric element of 1mm is as sensor in the region of a 300mm × 400mm thereon, and the material of sensor is P51, respectively called after S 1-S 6, each sensor coordinates is as shown in table 1.
Adopt the generation that disconnected plumbous mode simulated sound is launched on aluminium sheet, totally 5 acoustic emission dummy sources, are respectively P 1-P 5, each only generation acoustic emission dummy source, the position of each acoustic emission dummy source is as shown in table 2.
With S 3for master reference, other sensors are time sensor.When the magnitude of voltage that master reference monitors exceedes certain threshold value, think that acoustie emission event occurs, all the sensors starts to gather acoustic emission signal simultaneously.If Fig. 3 and Fig. 4 is piezoelectric sensor S respectively 1and S 3the acoustic emission dummy source P received 1the acoustic emission signal sent.After sensor collects acoustic emission signal, Morlet wavelet transformation is adopted to process acoustic emission signal, the ripple extracting each signal reaches the moment, and the secondary ripple between sensor and master reference of calculating reaches time difference, table 3 is depicted as the ripple using wavelet transformation to extract in 40kHz frequency and reaches time difference data.
With acoustic emission dummy source P 1for example, set up and measure vector Z=[31.1 64.4 34.4 66.9 5.1] t, adopt the vector of the localization method status recognition based on the sequential Monte Carlo algorithm X=[x invented s, y s, V g] t, Fig. 5 is the acoustic emission dummy source P of sequential Monte Carlo algorithm identification 1the x coordinate of position, Fig. 6 is the acoustic emission dummy source P of sequential Monte Carlo algorithm identification 1the y coordinate of position, Fig. 7 is the velocity of wave V of 40kHz guided wave composition in the acoustic emission signal of sequential Monte Carlo algorithm identification g.Table 4 is the positioning result of each acoustic emission dummy source.
The each sensor coordinates of table 1 (mm)
Sensor mark S 1 S 2 S 3 S 4 S 5 S 6
X coordinate 0 150 150 0 -150 -150
Y coordinate -200 -200 0 200 200 0
Table 2 each acoustic emission dummy source coordinate (mm)
Dummy source mark P 1 P 2 P 3 P 4 P 5
X coordinate 0 100 50 -100 -100
Y coordinate 0 -50 100 100 -150
Table 3 ripple reaches time difference data (μ s)
Δt 13 Δt 23 Δt 43 Δt 53 Δt 63
P 1 31.1 64.4 34.4 66.9 5.1
P 2 71.7 54.5 125.9 178.0 120.5
P 3 99.7 105.2 -18.5 52.7 52.4
P 4 32.0 80.9 -83.5 -104.4 -108.5
P 5 -112.4 -23.7 46.5 36.7 -82.4
Table 4 each acoustic emission dummy source positioning result (mm)
Dummy source mark P 1 P 2 P 3 P 4 P 5
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, also comprises the technical scheme be made up of above technical characteristic combination in any.
With above-mentioned according to desirable embodiment of the present invention for enlightenment, by above-mentioned description, relevant staff in the scope not departing from this invention technological thought, can carry out various change and amendment completely.The technical scope of this invention is not limited to the content on instructions, must determine its technical scope according to right.

Claims (6)

1. based on an acoustic emission source locating method for sequential Monte Carlo algorithm, for the location of the lower acoustic emission source of uncertain factor impact; It is characterized in that, comprise the following steps:
(1) for the acoustic emission source be in isotropy planar structure, N number of sensor for receiving the acoustic emission signal that acoustic emission source sends is arranged; In described each sensor, one of them is decided to be master reference, remaining is then decided to be time sensor; Aforesaid isotropy planar structure sets up plane coordinate system, makes the coordinate of acoustic emission source be (x s, y s), the coordinate of master reference is (x i, y i), the coordinate of each sensor is (x j, y j) (j=1 ... i-1, i+1...N), wherein, N is positive integer, N>=4;
(2) obtained the due in of the acoustic emission signal that each sensor receives by signal processing method, and calculate the due in difference Δ t between each sensor and master reference ij, formed and measure vector Z;
(3) position of acoustic emission source and corresponding acoustic emission signal velocity of wave is characterized with state vector X; Definition status vector X=[x s, y s, V g] t, wherein: (x s, y s) be the position coordinates of acoustic emission source, V gfor 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 measure equation and carry out iterative state vector X; Wherein:
System equation expression formula is
X k＝X k-1+ω k-1
Measuring equation expression formula is
Z k＝h(X k)+υ k
In formula: k is number of iterations; ω is system noise; υ is measurement noises; H is a nonlinear function, uses
In expressing the element Δ t measured in vector Z ijand the relation between the element in state vector X;
Δt i j = ( x s - x i ) 2 + ( y s - y i ) 2 - ( x s - x j ) 2 + ( y s - y j ) 2 V g
In formula: Δ t ijfor a jth secondary due between sensor and i-th sensor is poor;
(5) adopt sequential Monte Carlo analogy method to carry out numerical solution, iteration calculates state vector X estimated value, and the state vector X estimated value gone out using the last iteration meeting iterations is as acoustic emission source position (x s, y s) and corresponding velocity of wave V gdiscre value.
2. the acoustic emission source locating method based on sequential Monte Carlo algorithm according to claim 1, is characterized in that: described signal processing method is small wave converting method.
3. the acoustic emission source locating method based on sequential Monte Carlo algorithm according to claim 1, is characterized in that: the employing sequential Monte Carlo analogy method mentioned in step (5) to carry out the concrete steps of numerical solution estimated state vector X is:
(i) k=0, initialization N pindividual particle: according to planar structure size and material character determination prior distribution p (X 0), and from prior distribution p (X 0) random sampling acquisition and establish weight i=1 ..., N p;
(ii) k=k+1, random generation process noise from the distribution of system noise ω and adopt system equation to predict
(iii) calculate adopt and measure equation estimation upgrade weight w k i = w k - 1 i p ( Z k | X k i ) , And normalized weight w k i = w k i / Σ i = 1 N p w k i ;
(iv) effective sample quantity N is calculated effif, N effbe less than N p/ 2, start resampling program, with probability p ( X k i * = X k i ) = w k i , Generate one group of new particle p ( X k i * = X k i ) = w k i ;
V () calculates X kaverage as X kestimated value;
(vi) get back to step (ii), until the number of iterations that presets arrives or the condition of convergence meets, export last generation state vector X estimated value as final state vector X discre value.
4. the acoustic emission source locating method based on sequential Monte Carlo algorithm according to claim 3, is characterized in that: described iterations by presetting, or is determined by the condition of convergence; This condition of convergence is: the difference of the estimated value of continuous multiple iteration step is less than predetermined value.
5. the acoustic emission source locating method based on sequential Monte Carlo algorithm according to claim 3, is characterized in that: when setting up system equation, and the average of supposing the system noise ω is zero, and its covariance matrix is normal distribution; When setting up measurement equation, suppose that the average of measurement noises υ is zero, and its covariance matrix is normal distribution.
6. the acoustic emission source locating method based on sequential Monte Carlo algorithm according to claim 1, is characterized in that: the uncertain factor of acoustic emission source is model error and measurement noises.
CN201510338750.4A 2015-06-17 2015-06-17 Acoustic emission source locating method based on sequential Monte Carlo algorithm Active CN104914167B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510338750.4A CN104914167B (en) 2015-06-17 2015-06-17 Acoustic emission source locating method based on sequential Monte Carlo algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510338750.4A CN104914167B (en) 2015-06-17 2015-06-17 Acoustic emission source locating method based on sequential Monte Carlo algorithm

Publications (2)

Publication Number Publication Date
CN104914167A true CN104914167A (en) 2015-09-16
CN104914167B CN104914167B (en) 2017-09-19

Family

ID=54083415

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510338750.4A Active CN104914167B (en) 2015-06-17 2015-06-17 Acoustic emission source locating method based on sequential Monte Carlo algorithm

Country Status (1)

Country Link
CN (1) CN104914167B (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106093207A (en) * 2016-03-24 2016-11-09 南京航空航天大学 A kind of Lamb wave damage positioning method based on non-linear Unscented Kalman Filter algorithm
CN106199521A (en) * 2016-07-19 2016-12-07 中南大学 A kind of microseism based on minimum range or the abnormal then data identification method of acoustic emission
CN106526074A (en) * 2016-09-23 2017-03-22 天津大学 Method for detecting direction of source of three dimensional odor loaded on unmanned rotorcraft
CN110376290A (en) * 2019-07-19 2019-10-25 中南大学 Acoustic emission source locating method based on multidimensional Density Estimator
CN111398433A (en) * 2020-04-17 2020-07-10 中南大学 Acoustic emission source positioning method and system based on linear weighted least square method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2026137A1 (en) * 1989-11-02 1991-05-03 Hermann Grosch Procedure for determining the range and direction of sound-producing targets
CN1224870A (en) * 1998-03-20 1999-08-04 刘中华 Touch point position parameter information detection sensing device
CN101802636A (en) * 2007-09-13 2010-08-11 西门子公司 Method for increasing the location accuracy for unsynchronized radio subscribers
CN101806882A (en) * 2010-03-11 2010-08-18 沈阳化工学院 Locating method of emitting source
CN102129063A (en) * 2010-12-23 2011-07-20 中南大学 Method for positioning micro seismic source or acoustic emission source
CN102435980A (en) * 2011-09-15 2012-05-02 中南大学 Acoustic emission source or micro seismic source positioning method based on analytic solution
CN103235284A (en) * 2013-03-29 2013-08-07 中国气象科学研究院 Multi-station lightning VHF (very high frequency) radiation source three-dimensional positioning method and system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2026137A1 (en) * 1989-11-02 1991-05-03 Hermann Grosch Procedure for determining the range and direction of sound-producing targets
CN1224870A (en) * 1998-03-20 1999-08-04 刘中华 Touch point position parameter information detection sensing device
CN101802636A (en) * 2007-09-13 2010-08-11 西门子公司 Method for increasing the location accuracy for unsynchronized radio subscribers
CN101806882A (en) * 2010-03-11 2010-08-18 沈阳化工学院 Locating method of emitting source
CN102129063A (en) * 2010-12-23 2011-07-20 中南大学 Method for positioning micro seismic source or acoustic emission source
CN102435980A (en) * 2011-09-15 2012-05-02 中南大学 Acoustic emission source or micro seismic source positioning method based on analytic solution
CN103235284A (en) * 2013-03-29 2013-08-07 中国气象科学研究院 Multi-station lightning VHF (very high frequency) radiation source three-dimensional positioning method and system

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
曲从善，许化龙等: "非线性贝叶斯滤波算法综述", 《电光与控制》 *
李军科，张串，吴建军: "基于蒙特卡洛方法的粒子滤波算法研究", 《电脑与信息技术》 *
王伟魁，曾周末，李一搏: "大型原油储罐罐底声发射源定位算法研究", 《计算机工程与应用》 *
郝立亚: "基于蒙特卡洛模拟的贝叶斯随机波动模型及应用研究", 《中国博士学位论文全文数据库 经济与管理科学辑（月刊）》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106093207A (en) * 2016-03-24 2016-11-09 南京航空航天大学 A kind of Lamb wave damage positioning method based on non-linear Unscented Kalman Filter algorithm
CN106199521A (en) * 2016-07-19 2016-12-07 中南大学 A kind of microseism based on minimum range or the abnormal then data identification method of acoustic emission
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
CN106526074A (en) * 2016-09-23 2017-03-22 天津大学 Method for detecting direction of source of three dimensional odor loaded on unmanned rotorcraft
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
CN110376290A (en) * 2019-07-19 2019-10-25 中南大学 Acoustic emission source locating method based on multidimensional Density Estimator
CN111398433A (en) * 2020-04-17 2020-07-10 中南大学 Acoustic emission source positioning method and system based on linear weighted least square method

Also Published As

Publication number Publication date
CN104914167B (en) 2017-09-19

Similar Documents

Publication Publication Date Title
US8833153B2 (en) Correction of pressure signals measured during supersonic wind tunnel testing
CN106950276B (en) Pipeline defect depth inversion method based on convolutional neural network
CN104914167A (en) SMC (Sequential Monte Carlo) algorithm based acoustic emission source location method
Kaloop et al. Multi input–single output models identification of tower bridge movements using GPS monitoring system
CN103776416B (en) The indirect measurement method that a kind of antenna structure based on strain transducer is out of shape
CN105954720B (en) There are the radiation source time difference of passive detection observation station site error
CN102129063A (en) Method for positioning micro seismic source or acoustic emission source
CN106093207A (en) A kind of Lamb wave damage positioning method based on non-linear Unscented Kalman Filter algorithm
CN103542820B (en) A kind of method detecting wind-tunnel inside surface flatness
CN106959105A (en) Method for calibrating compass and device
CN106908755A (en) Wireless acoustic sensor network pushes the sound bearing method of estimation of contracting gas leakage
CN104537251A (en) Fan blade impulse load recognition method
Marineau et al. Sharp cone boundary-layer transition and stability at Mach 14
CN114218778A (en) Method and device for analyzing sonic boom test data
CN104794332B (en) A kind of Uncertainty Analysis Method of skyscraper wind-excited responese analysis model
CN103106332A (en) Analysis method of measurement uncertainty degree
CN102565541A (en) Recursion demodulation method for electrical tomography system
CN105510891A (en) In-orbit modal identification method for synthetic aperture radar of satellite
CN106874561B (en) Multidisciplinary uncertainty propagation analysis method based on Newton iteration
CN106772193B (en) Measuring method using current transformer frequency characteristic measuring device
CN110580391B (en) Fundamental frequency mode measurement method of flexible structure
Niri et al. Acoustic emission (AE) source localization using extended Kalman filter (EKF)
CN107084712A (en) Data processing method and device and method for calibrating compass and device
Hmad et al. Verification and validation of structural health monitoring algorithms: A maturation procedure
CN104198153A (en) Thermal environment test method for slender missile protrusions

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
CB03 Change of inventor or designer information

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

CB03 Change of inventor or designer information
GR01 Patent grant
GR01 Patent grant