CN115201326B - Defect burial depth multi-parameter identification method based on near-field vortex and far-field vortex - Google Patents
Defect burial depth multi-parameter identification method based on near-field vortex and far-field vortex Download PDFInfo
- Publication number
- CN115201326B CN115201326B CN202210992904.1A CN202210992904A CN115201326B CN 115201326 B CN115201326 B CN 115201326B CN 202210992904 A CN202210992904 A CN 202210992904A CN 115201326 B CN115201326 B CN 115201326B
- Authority
- CN
- China
- Prior art keywords
- eddy current
- field
- depth
- far
- defect
- 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.)
- Active
Links
- 230000007547 defect Effects 0.000 title claims abstract description 102
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000009933 burial Methods 0.000 title claims abstract description 10
- 238000001514 detection method Methods 0.000 claims abstract description 138
- 238000001228 spectrum Methods 0.000 claims abstract description 49
- 238000003066 decision tree Methods 0.000 claims abstract description 15
- 230000005284 excitation Effects 0.000 claims description 31
- 238000009825 accumulation Methods 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 2
- 238000007619 statistical method Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 3
- 238000005259 measurement Methods 0.000 abstract description 2
- 238000000605 extraction Methods 0.000 abstract 1
- 239000000523 sample Substances 0.000 description 36
- 239000002184 metal Substances 0.000 description 11
- 230000005291 magnetic effect Effects 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- 239000011248 coating agent Substances 0.000 description 3
- 238000000576 coating method Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 239000002131 composite material Substances 0.000 description 2
- 230000001808 coupling effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000003466 welding Methods 0.000 description 2
- 229910000851 Alloy steel Inorganic materials 0.000 description 1
- 229910000906 Bronze Inorganic materials 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000010974 bronze Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- KUNSUQLRTQLHQQ-UHFFFAOYSA-N copper tin Chemical compound [Cu].[Sn] KUNSUQLRTQLHQQ-UHFFFAOYSA-N 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000002500 effect on skin Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000003302 ferromagnetic material Substances 0.000 description 1
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 description 1
- 238000002513 implantation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000007847 structural defect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
- G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
- G01N27/90—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws using eddy currents
- G01N27/904—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws using eddy currents with two or more sensors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
- G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
- G01N27/90—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws using eddy currents
- G01N27/9006—Details, e.g. in the structure or functioning of sensors
Abstract
The invention discloses a defect burial depth multi-parameter identification method based on near-field eddy current and far-field eddy current, which comprises the following steps: 1. preparing M standard samples with different depth defects, and collecting eddy current signals by using an eddy current detector; 2. obtaining near field eddy current detection component IMF of mth sample using EMD and PCA x,m And far field eddy current detection component IMF y,m The method comprises the steps of carrying out a first treatment on the surface of the 3. Respectively extracting IMF x,m And IMF (inertial measurement unit) y,m Spectrum centroid characteristics of marginal spectrum of (2) and inverting near field defect detection depth x of the mth standard sample 1,m And far field defect detection depth x 2,m Determining the defect depth H of the mth standard sample m A relationship therebetween; 4. extraction of IMF x,m Impedance and IMF of (a) y,m Is determined and a threshold is determined; 5. and establishing a decision tree based on the relation and the threshold value, and analyzing the detection result of the detected sample to realize quantitative identification of the defect depth. The invention can realize the analysis and judgment of the defect depth of the detected sample and improve the identification efficiency and accuracy.
Description
Technical Field
The invention relates to the technical field of eddy nondestructive testing, in particular to a defect burial depth multi-parameter identification method based on near-field eddy and far-field eddy.
Background
The metal parts and the coating structures thereof are easy to generate defect damage with different degrees in the manufacturing and service processes, the defect forms are complex, the distribution positions have higher uncertainty, such as a hydraulic system piston part, the base material is alloy steel, the surface is a bronze wear-resistant layer, and various structural defects such as abrasion, scratch, sand holes, cracks, defects and the like are frequently generated; and the welding and bonding process between the wear-resistant layer and the base material often has defects such as incomplete welding, micro holes on the interface part, residual deformation, cracks, dislocation and the like. In order to ensure high quality and high reliability of the coating structure of the metal part in the service process, the metal part needs to be detected irregularly, the potential damage in the metal part is defined, the serious failure of the metal part is avoided, and serious accidents are prevented.
The eddy current detection has the advantages of high sensitivity, rapid response, convenient operation, low cost and the like, and is widely applied to the fields of defect detection of metal parts, subsurface crack characterization and the like. Because the eddy current has skin effect, the eddy current has high detection sensitivity to the defects on the surface or near surface of the metal part. However, the skin depth of the eddy current detection is inversely proportional to the magnetic permeability, the electric conductivity and the excitation frequency, the excitation frequency of the near-field eddy current is generally larger, and the skin depth is reduced along with the increase of the excitation frequency, so that the problem of deep defect detection cannot be solved by the near-field eddy current, and meanwhile, the detection frequency of the far-field eddy current is generally smaller, so that the deep defect can be detected, but the defect inside and outside a test piece cannot be effectively distinguished. Therefore, for the metal parts and the coating structures thereof with near-surface and internal defects, the defect information of the metal parts cannot be accurately reflected by a single near-field vortex or far-field vortex, and the required detection precision cannot be achieved, so that the damage state of the metal parts cannot be accurately evaluated in time, the metal parts are caused to fail in the service process, and major accidents are caused.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a defect burial depth multi-parameter identification method based on near-field eddy current and far-field eddy current so as to analyze and judge the defect depth, thereby improving identification efficiency and accuracy.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention discloses a defect burial depth multi-parameter identification method based on near-field and far-field eddy currents, which is characterized by comprising the following steps of:
s1, designing an eddy current detector:
the eddy current tester includes: the device comprises a receiving coil, a near-field eddy current excitation coil, a shielding layer and a far-field eddy current excitation coil; the near-field eddy current excitation coil is arranged on the outer side of the receiving coil, the shielding layer is arranged on the outer side of the near-field eddy current excitation coil, and the far-field eddy current excitation coil is arranged on the outer side of the shielding layer;
s2, preparing a standard sample and collecting eddy current signals:
m samples with preset numbers of 1,2, … and M are respectively prepared, and depth H is respectively prepared on the samples with corresponding numbers 1 、H 2 、…、H M So that one sample corresponds to one defect, thereby obtaining M standard samples with different depth defects, and H 1 ＜H 2 ＜…＜H M ；
Detecting M standard samples respectively by using the eddy current detector according to the same lifting height and angles perpendicular to the detection surface of the standard samples, so as to obtain detection signals of the M standard samples;
s3, signal processing:
respectively decomposing the mth detection signal by using an EMD method to obtain a plurality of connotation modal components, and then selecting a multi-order connotation modal component with larger energy ratio accumulation from the plurality of connotation modal components; m=1, 2, …, M;
counting the contribution accumulation rate between the multi-order connotation modal component and the mth detection signal by using a PCA method, and selecting two connotation modal components with the contribution accumulation rate larger than a set threshold value, thereby defining the connotation modal component with the larger frequency of the two connotation modal components as a near-field eddy current detection component IMF of the mth detection signal x,m The less frequent connotation mode component is defined as the far-field eddy current detection component IMF of the mth detection signal y,m ；
S4, extracting spectrum centroid characteristics of the marginal spectrum and inverting defect depth:
respectively for the near-field eddy current detection component IMF x,m And far field eddy current detection component IMF y,m Performing Hilbert transformation to correspondingly obtain a near-field Hilbert spectrum and a far-field Hilbert spectrum;
integrating the near-field Hilbert spectrum and the far-field Hilbert spectrum in time respectively to obtain a near-field Hilbert marginal spectrum and a far-field Hilbert marginal spectrum;
the spectrum centroid characteristics of the near-field Hilbert marginal spectrum and the far-field Hilbert marginal spectrum are respectively extracted, so that the near-field defect detection depth x of the mth standard sample is respectively inverted according to the spectrum centroid characteristics of the near-field Hilbert marginal spectrum and the far-field Hilbert marginal spectrum 1,m And far field defect detection depth x 2,m ；
S5, determining the defect depth H of the mth standard sample m And near field detection depth x 1,m Depth of far field detection x 2,m Relationship between:
if x 1,m ＝H m Then the defect depth H of the mth standard sample is represented m Namely the corresponding near field defect detection depth x 1,m And satisfies that M is more than or equal to 1 and less than or equal to M 1 ；
If x 2,m ＝H m Then the defect depth H of the mth standard sample is represented m Namely the corresponding far field defectDepth of detection x 2,m The method comprises the steps of carrying out a first treatment on the surface of the And satisfy M 2 ≤m≤M；
Otherwise, constructing the defect depth H of the mth standard sample by using a linear equation m Depth x of near field defect detection corresponding to 1,m And far field defect detection depth x 2,m Functional relationship between Ax 1,m +Bx 2,m ＝H m The method comprises the steps of carrying out a first treatment on the surface of the And satisfy M 1 ＜m＜M 2 Wherein 1 < M 1 ＜M 2 ＜M；M 1 And M 2 Representing two depth thresholds;
s6, extracting near-field eddy current detection component IMF x,m And determining an impedance thresholdAnd->
When m is E [1, M 1 ]At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
When M epsilon (M) 1 ，M 2 ) At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
When M is E [ M ] 2 ，M]At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
S7, extracting far-field eddy current detection component IMF y,m Is a phase difference characteristic of (2)And determining a phase threshold +.>And->
When m is E [1, M 1 ]When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
When M epsilon (M) 1 ，M 2 ) When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
When M is E [ M ] 2 ，M]When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
S8, establishing a decision tree according to the S5-S7, and quantitatively identifying the defect depth of the detected sample according to the decision tree:
and detecting the detected sample by using the eddy current detector, performing signal processing according to the process of the step S3 to obtain a near-field eddy current detection component and a far-field eddy current detection component of the detected sample, extracting the impedance characteristic of the near-field eddy current detection component and the phase difference characteristic of the far-field eddy current detection component of the detected sample, inputting the impedance characteristic and the phase difference characteristic of the far-field eddy current detection component into the decision tree, and obtaining a defect depth identification result of the detected sample.
The invention provides an electronic device, which comprises a memory and a processor, and is characterized in that the memory is used for storing a program for supporting the processor to execute the defect burial depth multi-parameter identification method, and the processor is configured to execute the program stored in the memory.
The invention relates to a computer readable storage medium, which is stored with a computer program, characterized in that the computer program executes the steps of the defect burial depth multi-parameter identification method when being run by a processor.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the detection requirement of the detected test piece, the near-field eddy current and far-field eddy current combined detection mode is adopted, and the coil unit with multiple excitation and single receiving is selected, so that the near-surface defects and the internal defects are detected simultaneously. The composite detection can detect the lower surface defect compared with the near-field eddy current, has more information of the upper surface defect, can rapidly distinguish the upper surface defect and the lower surface defect compared with the far-field eddy current system, and has higher detection precision on the upper surface defect.
2. The invention obtains the near-field eddy current detection component IMF by adopting EMD and PCA aiming at the detection results of standard samples with different depth defects x,m And far field eddy current detection component IMF y,m The method comprises the steps of carrying out a first treatment on the surface of the And extracting IMF x,m And IMF (inertial measurement unit) y,m Inverting the defect depth by using the spectrum centroid characteristics of the marginal spectrum of the (2), and determining the relation among the defect depth of the standard sample, the near-field detection depth and the far-field detection depth; based on IMF x,m Impedance characteristics and IMF of (a) y,m A phase difference characteristic determination threshold value; and establishing a decision tree based on the relation and the threshold value, and analyzing the detection result of the detected sample according to the decision tree, so that the quantitative identification of the defect depth of the detected sample is realized, and the identification efficiency and accuracy are improved.
Drawings
FIG. 1 is a schematic diagram of an eddy current tester for identifying multiple parameters of a defect implantation depth according to the present invention;
FIG. 2 is a schematic diagram of a decision tree model of near-field and far-field composite detection according to the present invention.
Reference numerals in the drawings: 1. a receiving coil; 2. a near field eddy current excitation coil; 3. a shielding layer; 4. the far field eddy current excites the coil.
Detailed Description
In this embodiment, as shown in fig. 1, a defect burial depth multi-parameter identification method based on near-field eddy current and far-field eddy current is implemented by processing detection signals by adopting EMD and PCA to obtain a near-field eddy current detection component and a far-field eddy current detection component; inverting the defect depth based on the spectrum centroid characteristics of the marginal spectrum, and determining the relationship between the defect depth of the standard sample and the near-field detection depth and the far-field detection depth; determining a threshold value based on the impedance characteristic of the near-field eddy current detection component and the phase difference characteristic of the far-field eddy current detection component; establishing a decision tree based on the relationship and the threshold; the method for quantitatively identifying the defect depth of the detected sample based on the decision tree comprises the following steps:
step 1, as shown in fig. 1, the eddy current tester includes: a receiving coil 1, a near-field eddy current excitation coil 2, a shielding layer 3 and a far-field eddy current excitation coil 4; a near-field eddy current excitation coil 2 is provided outside the receiving coil 1, a shield layer 3 is provided outside the near-field eddy current excitation coil 2, and a far-field eddy current excitation coil 4 is provided outside the shield layer 3. The near-field eddy current excitation coil, the far-field eddy current excitation coil and the receiving coil are magnetic cores with high magnetic conductivity, and the coils with high magnetic conductivity are wound outside the magnetic cores so as to reduce the number of turns of the coils and the size of the probe. The shielding layer can block direct coupling effect between the far-field eddy current excitation coil and the receiving coil, so that indirect coupling effect accounts for a main part. The shielding layer is made of ferromagnetic material with high magnetic conductivity and high electric conductivity, and the thickness is 2mm.
Step 2, preparing a standard sample and collecting eddy current signals: the size of the defect effect is difficult to carry out theoretical calculation due to the comprehensive influence of the defect position, depth and shape in the test piece, so that the test results of defects of different shapes, depths and positions of various materials under different frequencies are usually obtained by means of a model for test, and the basis is provided for actual test. M samples with preset numbers of 1,2, … and M are respectively prepared, and depth H is respectively prepared on the samples with corresponding numbers 1 、H 2 、…、H M Defects of (2) such thatOne sample corresponds to one defect, thereby obtaining M standard samples with different depth defects, and H 1 ＜H 2 ＜…＜H M . Excitation signals with different frequencies are respectively introduced into the near-field eddy current excitation coil and the far-field eddy current excitation coil, the standard sample is detected, and optimized detection frequencies of near-field detection and far-field detection are respectively determined. And the excitation signal generation module is regulated, proper voltage is selected and input into the signal generator, weak signals are amplified through the excitation signal amplification module, the amplified signals are input into the near-field eddy current excitation coil and the far-field eddy current excitation coil, and the excitation signals are regulated to optimal detection frequencies for near-field detection and far-field detection. And respectively detecting the M standard samples by using an eddy current detector according to the angle which is the same in lifting height and is perpendicular to the detection surface of the standard samples, so as to obtain detection signals of the M samples. And after the detected changed induced voltage passes through the detection signal processing module, the detected changed induced voltage is collected by a data collection card and is input into a signal processing center.
Step 3, signal processing: respectively decomposing the mth detection signal by using an EMD method to obtain a plurality of connotation modal components, wherein m=1, 2, …, M:
1) And respectively drawing an upper envelope line and a lower envelope line according to the upper extreme point and the lower extreme point of the signal.
2) And solving the average value of the upper envelope line and the lower envelope line, and drawing an average value envelope line.
3) And subtracting the mean envelope curve from the signal to obtain an intermediate signal.
4) Judging whether the intermediate signal meets two conditions of IMF, if so, the signal is an IMF component; if not, based on the signal, the analysis of 1) to 4) is made again.
The two conditions are respectively: (1) The number of extreme points and the number of zero crossings must be equal or differ by at most not more than one in the whole data segment. (2) At any time, the average value of the upper envelope formed by the local maximum points and the lower envelope formed by the local minimum points is zero, that is, the upper and lower envelopes are locally symmetrical with respect to the time axis.
After the first IMF is obtained by the method, IMF1 is subtracted by using the detection signal to be used as a new detection signal, IMF2 can be obtained by analyzing 1) to 4), and the like, and EMD decomposition is completed.
After the defect signal is decomposed by EMD, IMF components h (t) of each order are obtained, the distribution of signal energy in each frequency point is defined as an energy density function of each signal in unit frequency, and the energy density function is marked as G (w), so that the total energy of the signal h (t):
the spectral function of the signal is F (jw), which is obtained by Pasteur theorem:
the IMF component energy density function of each order can be expressed as:
G(w)＝|F(jw)| 2 (3)
energy ratio accumulation (energy ratio accumulation, ERA) is defined herein:
where n is the IMF component order and k represents the kth (k < n) order IMF component. The energy ratio accumulation is used as a standard for selecting the first k IMF components for analysis, and the k value when ERA is more than or equal to 95% is usually taken as the maximum IMF order. Selecting a multi-order connotation modal component with larger energy ratio accumulation from the plurality of connotation modal components;
and counting the contribution accumulation rate between the multi-order meaning modal component and the mth detection signal by using a PCA method, wherein a characteristic data set obtained by the defect signal comprises m groups of data and n acquisition points, and a sample matrix is marked as X.
The PCA modeling steps were as follows:
1) The sample matrix X is subjected to standardization processing to obtain a standardized sample matrix Z:
wherein i=1, 2, …, m; j=1, 2, …, n.
2) Calculating a correlation coefficient matrix delta of the standardized sample matrix:
3) If the eigenvalue is lambda, the eigenvalue of the correlation coefficient matrix delta can be listed, and m eigenvectors can be obtained from the eigenvalue.
|Δ-λIm|＝0 (7)
The eigenvalue lambda calculated i (i=1, 2, …, m) can obtain a unit feature vector P 1 ，P 2 ，…，P m 。
4) The main components are selected: the number of the final selected principal components is based on the characteristic value lambda i The contribution accumulation rate of the information amount is determined, and the contribution accumulation rate is defined herein:
where k is the number of determined eigenvalues.
Selecting two connotation mode components with contribution accumulation rate larger than a set threshold value, thereby defining the connotation mode component with larger frequency in the two connotation mode components as a near-field eddy current detection component IMF of an mth detection signal x,m The less frequent connotation mode component is defined as the far-field eddy current detection component IMF of the mth detection signal y,m 。
Step 4, extracting spectrum centroid characteristics of the marginal spectrum and inverting defect depth: for near field eddy current detection component IMF x,m And far field eddy current detection component IMF y,m The Hilbert transform is performed and,
by IMF x,m And H [ IMF ] x,m (t)]Resolving signals for conjugate complex pair structure
By IMF y,m And H [ IMF ] y,m (t)]Resolving signals for conjugate complex pair structure
Further, the instantaneous frequency can be obtainedThe method comprises the following steps:
correspondingly obtaining a near-field Hilbert spectrum and a far-field Hilbert spectrum:
integrating the near-field Hilbert spectrum and the far-field Hilbert spectrum in time respectively to obtain a near-field Hilbert marginal spectrum and a far-field Hilbert marginal spectrum respectively:
wherein: t represents the total length of the data.
Respectively extracting spectrum centroid characteristics of a near-field Hilbert marginal spectrum and a far-field Hilbert marginal spectrum;
the marginal spectrum centroid expression is:
thereby respectively inverting the near-field defect detection depth x of the mth standard sample according to the spectrum centroid characteristics of the near-field Hilbert marginal spectrum and the far-field Hilbert marginal spectrum 1,m And far field defect detection depth x 2,m ；
Step 5, determining the defect depth H of the mth standard sample m And near field detection depth x 1,m Depth of far field detection x 2,m Relationship between: judging x 1,m ＝H m Whether or not the equation is satisfied, if so, m E [1, M 1 ]The method comprises the steps of carrying out a first treatment on the surface of the If the equation is not satisfied, determine x 2,m ＝H m Whether or not the equation is satisfied, if so, M E [ M ] 2 ，M]The method comprises the steps of carrying out a first treatment on the surface of the If the equation is not satisfied, M ε (M 1 ，M 2 ) Constructing defect depth H of mth standard sample by using linear equation m Depth x of near field defect detection corresponding to 1,m And far field defect detection depth x 2,m Functional relationship between Ax 1,m +Bx 2,m ＝H m 。M 1 And M 2 Representing two depth thresholds.
I.e. if x 1,m ＝H m Then the defect depth H of the mth standard sample is represented m Namely the corresponding near field defect detection depth x 1,m And satisfies that M is more than or equal to 1 and less than or equal to M 1 ；
If x 2,m ＝H m Then the defect depth H of the mth standard sample is represented m Namely the corresponding far field defect detection depth x 2,m The method comprises the steps of carrying out a first treatment on the surface of the And satisfy M 2 ≤m≤M；
Otherwise, constructing the defect depth H of the mth standard sample by using a linear equation m Depth x of near field defect detection corresponding to 1,m And far field defect detection depth x 2,m Letter betweenThe numerical relationship, ax 1,m +Bx 2,m ＝H m The method comprises the steps of carrying out a first treatment on the surface of the And satisfy M 1 ＜m＜M 2 Wherein 1 < M 1 ＜M 2 ＜M；
Step 6, extracting a near-field eddy current detection component IMF x,m And determining an impedance thresholdAnd->Because of the near-field eddy current detection component IMF x,m The impedance characteristics of (a) gradually decrease with the increase of the defect depth, so that the near-field eddy current detection component IMF is extracted x,m As a threshold;
when m is E [1, M 1 ]At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
When M epsilon (M) 1 ，M 2 ) At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
When M is E [ M ] 2 ，M]At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
Step 7, extracting far-field eddy current detection component IMF y,m Is a phase difference characteristic of (2)And determining a phase threshold +.>And->Because of far-field eddy current detection component IMF y The phase difference characteristic gradually increases with the increase of the defect depth, so that the field eddy current detection component IMF is extracted y The phase difference characteristic is used as a threshold value;
when m is E [1, M 1 ]When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
When M epsilon (M) 1 ，M 2 ) When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
When M is E [ M ] 2 ，M]When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
Step 8, a decision tree is established according to the steps 5 to 7, as shown in fig. 2, so that the defect depth of the detected sample is quantitatively identified according to the decision tree: and (3) detecting the detected sample by using an eddy current detector, performing signal processing according to the process of the step (3) to obtain a near-field eddy current detection component and a far-field eddy current detection component of the detected sample, extracting the impedance characteristic of the near-field eddy current detection component and the phase difference characteristic of the far-field eddy current detection component of the detected sample, inputting the impedance characteristic and the phase difference characteristic of the far-field eddy current detection component into a decision tree, and obtaining a defect depth identification result of the detected sample.
In this embodiment, an electronic device includes a memory for storing a program for supporting the processor to execute the defect-depth multi-parameter identification method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer readable storage medium stores a computer program, which when executed by a processor, performs the steps of the defect depth multi-parameter identification method.
Claims (3)
1. A defect burial depth multi-parameter identification method based on near-field eddy current and far-field eddy current is characterized by comprising the following steps:
s1, designing an eddy current detector:
the eddy current tester includes: the device comprises a receiving coil (1), a near-field eddy current excitation coil (2), a shielding layer (3) and a far-field eddy current excitation coil (4); the near-field eddy current excitation coil (2) is arranged on the outer side of the receiving coil (1), the shielding layer (3) is arranged on the outer side of the near-field eddy current excitation coil (2), and the far-field eddy current excitation coil (4) is arranged on the outer side of the shielding layer (3);
s2, preparing a standard sample and collecting eddy current signals:
m samples with preset numbers of 1,2, … and M are respectively prepared, and depth H is respectively prepared on the samples with corresponding numbers 1 、H 2 、…、H M So that one sample corresponds to one defect, thereby obtaining M standard samples with different depth defects, and H 1 ＜H 2 ＜…＜H M ；
Detecting M standard samples respectively by using the eddy current detector according to the same lifting height and angles perpendicular to the detection surface of the standard samples, so as to obtain detection signals of the M standard samples;
s3, signal processing:
respectively decomposing the mth detection signal by using an EMD method to obtain a plurality of connotation modal components, and then selecting a multi-order connotation modal component with larger energy ratio accumulation from the plurality of connotation modal components; m=1, 2, …, M;
statistical analysis of the samples using PCA methodThe contribution accumulation rate between the multi-order content modal component and the mth detection signal is selected, and two content modal components with the contribution accumulation rate larger than a set threshold value are selected, so that the content modal component with the larger frequency in the two content modal components is defined as a near-field eddy current detection component IMF of the mth detection signal x,m The less frequent connotation mode component is defined as the far-field eddy current detection component IMF of the mth detection signal y,m ；
S4, extracting spectrum centroid characteristics of the marginal spectrum and inverting defect depth:
respectively for the near-field eddy current detection component IMF x,m And far field eddy current detection component IMF y,m Performing Hilbert transformation to correspondingly obtain a near-field Hilbert spectrum and a far-field Hilbert spectrum;
integrating the near-field Hilbert spectrum and the far-field Hilbert spectrum in time respectively to obtain a near-field Hilbert marginal spectrum and a far-field Hilbert marginal spectrum;
the spectrum centroid characteristics of the near-field Hilbert marginal spectrum and the far-field Hilbert marginal spectrum are respectively extracted, so that the near-field defect detection depth x of the mth standard sample is respectively inverted according to the spectrum centroid characteristics of the near-field Hilbert marginal spectrum and the far-field Hilbert marginal spectrum 1,m And far field defect detection depth x 2,m ；
S5, determining the defect depth H of the mth standard sample m And near field detection depth x 1,m Depth of far field detection x 2,m Relationship between:
if x 1,m ＝H m Then the defect depth H of the mth standard sample is represented m Namely the corresponding near field defect detection depth x 1,m And satisfies that M is more than or equal to 1 and less than or equal to M 1 ；
If x 2,m ＝H m Then the defect depth H of the mth standard sample is represented m Namely the corresponding far field defect detection depth x 2,m The method comprises the steps of carrying out a first treatment on the surface of the And satisfy M 2 ≤m≤M；
Otherwise, constructing the defect depth H of the mth standard sample by using a linear equation m Depth x of near field defect detection corresponding to 1,m And far field defect detection depth x 2,m Function switch betweenThe system, ax 1,m +Bx 2,m ＝H m The method comprises the steps of carrying out a first treatment on the surface of the And satisfy M 1 ＜m＜M 2 Wherein 1 < M 1 ＜M 2 ＜M；M 1 And M 2 Representing two depth thresholds;
s6, extracting near-field eddy current detection component IMF x,m And determining an impedance thresholdAnd->
When m is E [1, M 1 ]At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
When M epsilon (M) 1 ，M 2 ) At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
When M is E [ M ] 2 ，M]At the time, near field eddy current detection component IMF x,m The impedance Z of (2) satisfies
S7, extracting far-field eddy current detection component IMF y,m Is a phase difference characteristic of (2)And determining a phase threshold +.>And->
When m is E [1, M 1 ]When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
When M epsilon (M) 1 ，M 2 ) When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
When M is E [ M ] 2 ，M]When in use, the far field eddy current detection component IMF y,m Is of the phase difference of (2)Satisfy->
S8, establishing a decision tree according to the S5-S7, and quantitatively identifying the defect depth of the detected sample according to the decision tree:
and detecting the detected sample by using the eddy current detector, performing signal processing according to the process of the step S3 to obtain a near-field eddy current detection component and a far-field eddy current detection component of the detected sample, extracting the impedance characteristic of the near-field eddy current detection component and the phase difference characteristic of the far-field eddy current detection component of the detected sample, inputting the impedance characteristic and the phase difference characteristic of the far-field eddy current detection component into the decision tree, and obtaining a defect depth identification result of the detected sample.
2. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to perform the defect onset multi-parameter identification method of claim 1, the processor being configured to execute the program stored in the memory.
3. A computer readable storage medium having a computer program stored thereon, characterized in that the computer program when run by a processor performs the steps of the defect depth multi-parameter identification method of claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210992904.1A CN115201326B (en) | 2022-08-18 | 2022-08-18 | Defect burial depth multi-parameter identification method based on near-field vortex and far-field vortex |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210992904.1A CN115201326B (en) | 2022-08-18 | 2022-08-18 | Defect burial depth multi-parameter identification method based on near-field vortex and far-field vortex |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115201326A CN115201326A (en) | 2022-10-18 |
| CN115201326B true CN115201326B (en) | 2024-03-29 |
Family
ID=83585790
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210992904.1A Active CN115201326B (en) | 2022-08-18 | 2022-08-18 | Defect burial depth multi-parameter identification method based on near-field vortex and far-field vortex |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115201326B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105241952A (en) * | 2015-10-30 | 2016-01-13 | 湘潭大学 | Pipeline elbow detection method based on remote field eddy current and detection device |
| CN113624834A (en) * | 2021-08-11 | 2021-11-09 | 合肥工业大学 | Defect depth identification method and system based on marginal spectrum centroid detection |
| WO2021223423A1 (en) * | 2020-05-07 | 2021-11-11 | 南京航空航天大学 | Parameter design method for high-speed rail defect detection system based on differential eddy current |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015136379A2 (en) * | 2014-03-14 | 2015-09-17 | Cgg Services Sa | Method and apparatus for estimating source signature in shallow water |
-
2022
- 2022-08-18 CN CN202210992904.1A patent/CN115201326B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105241952A (en) * | 2015-10-30 | 2016-01-13 | 湘潭大学 | Pipeline elbow detection method based on remote field eddy current and detection device |
| WO2021223423A1 (en) * | 2020-05-07 | 2021-11-11 | 南京航空航天大学 | Parameter design method for high-speed rail defect detection system based on differential eddy current |
| CN113624834A (en) * | 2021-08-11 | 2021-11-09 | 合肥工业大学 | Defect depth identification method and system based on marginal spectrum centroid detection |
Non-Patent Citations (2)
| Title |
|---|
| 可燃药筒粘接质量的超声无损检测研究;张志强;中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑;20210715;25-65 * |
| 飞机多层结构内层腐蚀损伤的远场涡流检测与评估;李小丽;陈新波;时建云;王莉;;无损检测;20200710(第07期);41-46 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115201326A (en) | 2022-10-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hosseini et al. | Application of time–frequency analysis for automatic hidden corrosion detection in a multilayer aluminum structure using pulsed eddy current | |
| CN109142514B (en) | Defect detection device and method based on pulse eddy current array | |
| CN110702783A (en) | Array eddy current method for detecting thermal fatigue cracks of water-cooled wall tube | |
| CN103336049A (en) | Pulsed eddy current detection method and device for eliminating lift-off effect | |
| Chen et al. | Inversion method in pulsed eddy current testing for wall thickness of ferromagnetic pipes | |
| CN109444257B (en) | Pulse eddy current detection device and method based on frequency domain lift-off intersection point | |
| Chen et al. | Study on defect classification in multi-layer structures based on Fisher linear discriminate analysis by using pulsed eddy current technique | |
| CN108008006B (en) | Welding seam defect detection method, device, equipment and system | |
| CN111948279A (en) | Quantitative evaluation method for weak magnetic detection of paramagnetic metal material crack defects | |
| CN110568059B (en) | Nondestructive testing method and device for steel wire rope | |
| EP3951381B1 (en) | Non-destructive testing method and device for testing and distinguishing internal and external defects of steel wire rope | |
| Tamhane et al. | Feature engineering of time-domain signals based on principal component analysis for rebar corrosion assessment using pulse eddy current | |
| CN117235433B (en) | Metal surface crack quantitative evaluation method based on alternating current magnetic field | |
| Chen et al. | Characterization of image sequences of a defect using pulsed eddy current signals | |
| CN115201326B (en) | Defect burial depth multi-parameter identification method based on near-field vortex and far-field vortex | |
| Yang et al. | Reliable characterization of bearing rings using Eddy current and Barkhausen noise data fusion | |
| CN109613111A (en) | A kind of gradient spiral vortex detection coil and its detection method | |
| CN115166026A (en) | System and method for identifying and classifying structural damage of carbon fiber composite board | |
| Lang et al. | A system identification based approach for pulsed eddy current non-destructive evaluation | |
| Deng et al. | Simulation analysis of eddy current testing parameters for surface and subsurface defect detection of aviation aluminum alloy plate | |
| JPH0641938B2 (en) | Nondestructive measurement method for zirconium alloy materials | |
| Majidnia et al. | A numerical study of the depth of penetration of eddy currents | |
| CN109632944A (en) | A kind of multilayer pipe string structure pulse eddy nondestructive testing method based on assemblage characteristic | |
| CN111458400A (en) | Metal material defect detection system based on electromagnetic induction | |
| Shuhao et al. | Method of imaging and evaluation for defect based on pulsed eddy current |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |