CN101701934A - ACFM intelligent visual defect detection system - Google Patents

ACFM intelligent visual defect detection system Download PDF

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CN101701934A
CN101701934A CN200910230601A CN200910230601A CN101701934A CN 101701934 A CN101701934 A CN 101701934A CN 200910230601 A CN200910230601 A CN 200910230601A CN 200910230601 A CN200910230601 A CN 200910230601A CN 101701934 A CN101701934 A CN 101701934A
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defective
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signal
defect
magnetic
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陈国明
李伟
倪春生
李文艳
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China University of Petroleum East China
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China University of Petroleum East China
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Abstract

The invention discloses an intelligent visual defect detection system based on an AC magnetic field. The system comprises a probe, an auxiliary circuit and system detection software and uses a defect quantification and shape reconstruction algorithm. An excitation part of the probe adopts a double-U-shaped structural design and consists of a mangan zinc ferrite iron core and two sets of orthogonal current-carrying coils, a detection part adopts a two-dimensional array structure, and the array probe adopts a lifting-releasing type traveling mode. The hardware processing of detection signals comprises signal conditionings such as amplification, filtering, and the like and also introduces an original excitation signal as a reference signal to carry out phase sensitive detection and low-pass filtering on the detection signals. The signals are sent into a computer through an A/D acquisition card, then the detection software is used for carrying out digital filtering and correlation analysis on the signals to draw a magnetic induction intensity curve and a butterfly diagram in real time, and size quantification and three-dimensional shape inversion are realized through the defect quantification and the shape reconstruction algorithm. The intelligent visual defect detection system gives a full play of the advantages of the computer and greatly improves the intellectualized and visual levels of AC magnetic field detection.

Description

The ACFM intelligent visual defect detection system
Technical field
The present invention relates to a kind of intelligent visual nondestructive detection system, more particularly, the present invention relates to ACFM intelligent visual defect detection system device, software and defective intelligence and quantize and method for visualizing based on the ac magnetic field detection technique.
Background technology
It is a kind of emerging nondestructiving detecting means that ac magnetic field detects (ACFM), and the surface crack detection at various occasion members such as petrochemical complex, Aero-Space is particularly suited for members such as offshore platform and submerged structure.But, at present, the ACFM The Application of Technology does not also realize the robotization of signal Processing, normally manually carry out analyzing and processing by means of experience, not only efficient is low, and the accuracy of quantitative analysis of defective also is difficult to improve, and visual aspect is still with magnetic flux density curve and butterfly diagram and represents defective, the shape information that can not reflect defective, visual level is not high.
Summary of the invention
The objective of the invention is to overcome the various deficiencies that exist in existing ac magnetic field pick-up unit and the detection method, provide a kind of ac magnetic field to detect the quantification and the method for visualizing of defective, realize the automatic quantification of detection signal, improve detection efficiency, accuracy of detection, realize the reconstruct of defect shape, improve the visual level that detects.
The present invention includes ac magnetic field pick-up unit and defective method for visualizing two parts.The ac magnetic field detection system mainly is made up of several parts such as power supply, excitation signal generator, power amplification circuit, incentive probe, magnetic test coil, signal conditioning circuit, phase shifter, phase-sensitive detection circuit, low-pass filter circuit, A/D capture card and computing machines as shown in Figure 1.Exciting signal source selects the accurate voltage-controlled function generator ICL8038 of American I NTERSIL company and radio frequency follower to produce, and 6KH is provided ZSinusoidal wave as pumping signal.This signal is through power amplification and phase shifter 90 0Import incentive probe after the phase shift respectively, incentive probe is made up of two U type manganese-zinc ferrite iron cores and two groups of quadrature current-carrying coils, structure as shown in Figure 2, two groups of current-carrying coils differ 90 by before and after the phase shift respectively 0The quadrature excitation signal drive respectively, motivate the at the uniform velocity uniform induction electric current of rotation at surface of the work, produce the uniform induction magnetic field that magnetic direction at the uniform velocity rotates, this structure incentive probe has effectively remedied the restriction of inductive current direction to direction of check, reach the purpose that any direction crackle is all had higher detection sensitivity, direction of check is detected becomes possibility.Magnetic test coil adopts 2 dimension arrayed, and array probe adopts the lifting type walking along the crack length direction, and near the magnetic induction density component the extraction thin sheet surface crackle is represented magnetic induction density with voltage form.Detection signal is finished rough handling through signal conditioning circuits such as amplification, filtering, introduces original excitation signal as the reference signal, and detection signal is carried out phase sensitive detection and low-pass filtering.The A/D capture card is converted into digital signal with simulating signal and sends into computing machine, by the digital signal processing software module that under the LabVIEW environment, designs, by computing machine signal is carried out digital filtering, correlation analysis, realize the vector mensuration of signal, and ACFM intelligent visual defect detection software by developing, real-time rendering magnetic induction density curve and butterfly diagram are realized the real time discriminating of defective, are quantized and the defective visual description automatically.
ACFM intelligent visual defect detection method comprises implement to draw the magnetic induction density curve and several partial functions such as butterfly diagram, defective are judged in real time, quantification automatically, angle calculation and shape are visual, can realize by following technical step:
(1) will pop one's head in after the signal gathered handles through amplification, filtering, A/D conversion and digital signal processing module, and draw out the magnetic induction density curve, and magnetic induction density two component quadratures are obtained butterfly diagram; According to magnetic induction density curve and butterfly diagram,, realize the automatic judgement and the warning of defective according to the real-time automatic judging method of defective of considering phase information;
(2) determine that defective exists after, at first cut apart thought based on finite element, determine that the field scan curve extracts step-length, discretize detects the field signal of gained, and the scanning probe path is divided into n part, length is respectively L n={ l 1, l 2, l 3..., l n, extract n magnetic signature vector B m[i], i=1,2,3 ..., n;
(3) according to the quantitative recognizer of generalized regression nerve networks (GRNN) defective intelligence, by the proper vector B of defective field signal mCalculate maximum length L and the maximum depth value D and the breadth extreme C of defective,, calculate the defect shape parameter value P[i on each analysis site according to the space conversion operator of field signal distribution with defect shape]={ L i, D i, C 1i, C 2i, wherein, L iRepresent this coordinate, D in the defect length direction iThe depth direction coordinate figure of representing this projection on defective cross sectional shape profile, C 1iAnd C 2iThe Width coordinate figure of representing this projection on the defect map surface shape profile respectively;
(4) utilize the defect shape parameter P that obtains in limited element calculation model and the step (3) to make up the ACFM Simulation Calculation, calculate, extract same analysis and put locational magnetic field simulation result B through finite element simulation c[i], i=1,2,3 ..., n;
(5) design object majorized function equation and optimal conditions judge whether the error ε between simulation result and the testing result is not more than threshold epsilon shown in following formula 0, perhaps cut apart quantity n and reach maximal value n Max
ϵ = 1 n · Σ i = 1 n ( B c [ i ] - B m [ i ] ) 2
ε≤ε 0Or n 〉=n Max
(6) if step (5) result is for being then to proceed next step: the result then resets analytical parameters for denying, and gets back to step (2);
(7) output defect shape parameter P is according to the cross section and the surperficial two-dimensional shapes of this parameter reconstruct defective; And scan the three-dimensional approximate shapes that obtains defective by outline line.
(9) all results calculate and finish, and supply to observe with numerical value, curve or graphics mode output result.
Signals collecting in this method realization flow step (1), for the magnetic field value of eliminating a certain collection point suddenlys change to the influence of defective cross sectional shape inverting, the employing area-method is calculated the field signal value on each route segment.Determine first in the step (2) that the signal analysis step-length is the scope automatic setting of defective in the magnetic flux density curve drawn automatically according to software and the butterfly diagram.The proper vector of the electromagnetic field signal that extracts in the step (2), be by the method for testing and emulation combines, by a large amount of defect sample experiments, regularity according to the defective making, variation from a certain change in size of defective (other factors is constant) analysis of magnetic field signal feature, sum up Changing Pattern, thereby filter out the flaw indication characteristic quantity.Defective in the step (3) quantitatively identification is to be finished by generalized regression nerve networks (GRNN) study automatically, training, obtains the GRNN network model of the conversion from the magnetic signature amount to flaw size; The two-dimensional shapes inverting of defective is on the quantifying defects basis, cuts apart thought by finite element, forms the two-dimensional shapes of genetic defects with the little defective stack of semiellipse of the standard shape of n.
ACFM intelligent visual defect detection system of the present invention can carry out non-contact detecting to the various surface imperfection of hardware, along with the scanning of probe judges that in real time whether defective exists, draws magnetic induction density curve and butterfly diagram, calculate flaw size automatically and draw defective two dimension 3D shape.Cost of the present invention is low, method is simple, gives full play to the advantage of computing machine, can realize the quantification and the Shape Reconstruction of defective, has improved the intellectuality and the visual degree of ACFM defects detection greatly.
Description of drawings:
Figure 1A CFM intelligent visual defect detection system synoptic diagram;
The double-U-shaped quadrature excitation sonde configuration of Fig. 2 figure;
Fig. 3 ACFM intelligent visual defect detects software flow pattern
Fig. 4 inverting crackle and real crack silhouette contrast figure;
Fig. 5 defective three-D profile sweeping figure.
Embodiment:
The invention will be further described below by drawings and Examples.
It is long to utilize the ACFM intelligent visual defect detection system to detect mild-steel sheet surface 40mm, and 8mm is dark, the crackle that 1mm is wide.
The intelligent visual defect testing process.The ACFM intelligent visual defect detection system is formed as shown in Figure 1, mainly detects several parts such as software by power supply, excitation signal generator, power amplification circuit, phase-shift circuit, incentive probe, detection probe, signal conditioning circuit, A/D capture card with based on the intelligent visual defect of LabVIEW and forms.Stabilized voltage supply is the signal generator power supply that voltage-controlled function generator ICL8038 and radio frequency follower are formed, and it is 6KH that signal generator provides frequency ZAmplitude be the 1V sine wave as pumping signal, this signal forms one group of phasic difference 90 mutually through power amplifier with phase shifter 0The quadrature excitation signal, drive two groups of drive coils of double-U-shaped quadrature excitation probe respectively, as shown in Figure 2, along with moving of probe motivates the uniform induction electric current that direction is made Periodic Rotating on the metal works surface, the magnetic test coil formation detection probe array that two-dimensional array is arranged is pressed close to workpiece at the center of double-U-shaped incentive probe and is laid, to extract the disturbance of the induction field that crackle causes, detected electromagnetic signal indicates with voltage value, and be delivered to signal conditioning circuit, through amplifying, signal conditioning circuits such as filtering are finished rough handling, through the A/D capture card simulating signal is converted into digital signal and sends into computing machine, detect software by the ACFM intelligent visual signal is carried out digital filtering, digital signal processing such as cross-correlation vector detection, real-time rendering magnetic induction density curve and butterfly diagram, and realize defective intelligence quantitatively identification and the visual inverting of defect shape.
Determining defects.Step 1.According to the probe acquired signal, through handling, real-time rendering goes out magnetic density curve and butterfly diagram, and the position of qualitative analysis defective easily and scope are extracted corresponding flaw indication.
Eigenwert is chosen.Step 2.Select B for use ZComponent waveform peak valley spacing L Z, away from cracks B XComponent signal amplitude B X0And B XSignal amplitude minimum value B XminAnd B ZSignal distortion maximum amplitude B Zmax, these four characteristic quantities are as the proper vector of describing crackle physical dimension information, and the notion of introducing sensitivity, definition B XSignal sensitivity S XBe B XThe trough degree of depth M of signal XB during with flawless XSignal amplitude B X0Ratio; B ZSignal sensitivity S ZBe B ZSignal distortion maximal value B ZmaxB during with flawless XSignal amplitude B X0Ratio, be shown below.
S X = M X B X 0 = B X 0 - B X min B X 0 , S Z = B Z max B X 0
The introducing of sensitivity not only can reduce the number of characteristic quantity, simplifies inverting, and can compensate the detection error effectively, improves inversion accuracy.
Crackle profile deepest point characteristic of correspondence vector is in this example: B m={ S X, S Z, L Z}={ 15.51%, 35.6%, 37.78mm}.
Crackle is quantitatively discerned.In conjunction with the defective define method in the assessment standards such as DNV, at the ACFM technical characterstic, adopt defective artificial and analogue simulation to combine, defective has been carried out classification makes, set up the defect characteristic storehouse, and with this feature database as training and test samples, to generalized regression nerve networks (GRNN) model training with check, set up the GRNN network model that crackle quantizes, the input signal eigenwert calculates crack size automatically.
Inverting gained flaw size is in this example: long 38.72mm; Dark 7.31mm; Wide 0.94mm.
Crackle is visual.Correlativity according to the distribution of the field signal above defect shape and the defective, Space Nonlinear from the field signal to the defect shape can be transformed the operator linearization, the crackle that obtains according to this linear operator and previous step falls into size, can calculate the crack shape parametric array, this parameter matrix is made up of crackle profile coordinate.
The crackle profile coordinate that calculates in this example is as shown in table 1.
The design object function.The error in judgement size, step (4) and (5).Can set up the defective mathematical model according to the defect shape parametric array that step (3) obtains, utilize Finite Element Method to calculate the theoretical value of field signal, adopt optimization method to set up the error judgment objective function, as the optimization equation among Fig. 3.The field signal value that FEM (finite element) calculation obtains also deposits in the basic database.
The reconstruct defective.Step (7).With each the group coordinate of the crack shape parametric array after optimizing crackle as the half-oval shaped of a standard, two dimension, three-dimensional profile profile that each semiellipse crackle superposes and just can obtain crackle, as shown in Figure 4 and Figure 5.
Interpretation of result.
The comparison chart of true cross sectional shape of crackle and inverting gained profile as shown in Figure 4, the three-D profile of crackle as shown in Figure 5, the error analysis of process table 1 as can be known, defect shape inverting mean absolute error is 0.4803mm, average relative error is 6%, and inversion accuracy surpasses 90%, and this algorithm inverting gained shape can be described the true cross sectional shape of defective comparatively accurately, and reduce scanning step, increase cutting umber n and can also further improve inversion accuracy.
Find by a large amount of test experience, ACFM intelligent visual defect detection system of the present invention can effectively detect metal plate surface imperfection size and dimension, and quantization error is less than 10%, and this system algorithm is simple, real-time, intelligent visual degree height.
Table 1 inverting crack shape data and the contrast of real crack data
Figure G2009102306010D0000051
Figure G2009102306010D0000061

Claims (9)

1. based on the intelligent visual defect pick-up unit of ac magnetic field, it is characterized in that the probe driver unit adopts double-U-shaped structural design, is made up of manganese-zinc ferrite iron core and two groups of quadrature current-carrying coils, 2 dimension array structures are adopted in the test section, and this array probe adopts the lifting type walking manner.
2. detection software is characterized in that, adopts the wavelet packet changing method that the ac magnetic field detection signal is carried out digital signal processing, utilizes the correlation analysis method of cross-spectral density, realizes that signal phase detects.
3. the defective intelligence based on the ac magnetic field detection technique quantizes and method for visualizing, it is characterized in that this method may further comprise the steps:
(1) will pop one's head in after the signal gathered handles through amplification, filtering, A/D conversion and digital signal processing module, draw out magnetic induction density curve and butterfly diagram; And, realize the automatic judgement and the warning of defective according to magnetic induction density curve and butterfly diagram;
(2) determine that the field scan curve extracts step-length, discretize detects the field signal of gained, extracts the magnetic signature vector;
(3) according to the magnetic signature value, intelligent quantify defects size is calculated the defect shape parameter;
(4) the defect shape parameter that obtains according to step (3) makes up finite element model FEM, calculating magnetic field density theory value;
(5) design object function judges whether to meet the demands;
(6) if step (5) result is for being to proceed next step, otherwise calculating step parameter and objective function are released next step parameter result about the derivative of defect parameters, get back to step (2);
(7), obtain the defective 3D shape according to gained defect shape parameter reconstruct defective;
(8) all results calculate and finish, and supply to observe with numerical value, curve or graphics mode output result.
4. the automatic judgement and the warning of defective according to claim 3, it is characterized in that, according to the real-time automatic judging method of defective of considering phase information, at first the distance between check point and the butterfly type figure initial point is greater than secure threshold, and the phase place of the current magnetic field signal that obtains of coherent detection also must could illustrate that there is defective in this position greater than the phase place thresholding.
5. magnetic signature vector according to claim 3 extracts, and it is characterized in that, introduces sensitivity, has defined magnetic-field component sensitivity, and compensation detects error effectively.
6. flaw size intelligence according to claim 3 quantizes, and it is characterized in that set up the GRNN network model of quantifying defects, the input signal eigenwert calculates flaw size automatically.
7. defect shape calculation of parameter according to claim 3, it is characterized in that, cut apart thought by finite element, defect shape is regarded as the stack of the little defective of a plurality of standards, the size defectiveness quantization algorithm of each little defective calculates and can get, and then these little flaw size values are formed the defect shape parametric array through the compensation of defect shape operator.
8. objective function according to claim 3 is characterized in that whether the error between finite element simulation result and the testing result is not more than threshold value, perhaps cuts apart quantity and whether reaches maximal value.
9. defective reconstruct according to claim 3 is characterized in that, the defective two-dimensional silhouette is that the unit carries out finite element stack and obtains with the standard half-oval shaped, and obtains 3D shape according to the two-dimensional silhouette sweeping.
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WO2023280023A1 (en) * 2021-07-08 2023-01-12 中国石油大学(华东) Underwater structure crack propagation visual monitoring system based on alternating-current electromagnetic field, and alternating-current electromagnetic field crack visual monitoring and evaluation method
CN113390954B (en) * 2021-07-08 2023-06-13 中国石油大学(华东) Underwater structure crack growth visual monitoring system based on alternating current electromagnetic field
CN113552211A (en) * 2021-07-14 2021-10-26 安徽工业大学 Crack direction identification method based on anisotropic conductive medium magnetic field regulation
CN115575488A (en) * 2022-11-21 2023-01-06 中国石油大学(华东) Integrated probe based on ACFM and vision and cooperative detection method
CN115963038A (en) * 2022-12-14 2023-04-14 中国科学院空间应用工程与技术中心 Magnetic particle motion track measuring system and method based on space microgravity condition
CN115963038B (en) * 2022-12-14 2023-07-28 中国科学院空间应用工程与技术中心 Magnetic particle motion trail measurement system and method based on space microgravity condition
CN115752221A (en) * 2023-01-07 2023-03-07 山东省三鼎汽车配件有限公司 Brake disc roundness detection device
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