CN102706955B - Pipeline defect characteristic extraction method and device based on uniaxial magnetic leakage data - Google Patents
Pipeline defect characteristic extraction method and device based on uniaxial magnetic leakage data Download PDFInfo
- Publication number
- CN102706955B CN102706955B CN201210177825.1A CN201210177825A CN102706955B CN 102706955 B CN102706955 B CN 102706955B CN 201210177825 A CN201210177825 A CN 201210177825A CN 102706955 B CN102706955 B CN 102706955B
- Authority
- CN
- China
- Prior art keywords
- defect
- single shaft
- image
- processing module
- magnet
- 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.)
- Expired - Fee Related
Links
Abstract
The invention discloses a pipeline defect characteristic extraction method and a pipeline defect characteristic extraction device based on uniaxial magnetic leakage data. The device comprises an internal detection device main body, a magnet, a uniaxial Hall sensor and a control unit circuit board, wherein the control unit circuit board comprises an analog switch, a voltage follower, a low-pass filter, an analog to digital (A/D) conversion module, a data signal processing (DSP) module, a field programmable gate array (FPGA) and a defect characteristic memory. The pipeline defect characteristic extraction method comprises the following steps of: converting the uniaxial magnetic leakage data of a pipeline internal detection device, which serve as characteristic extraction data, into an image according to a mapping relation between magnetic field intensity and pixels, and filtering the image; and determining the possible position of a defect by judgment, binarizing the image by using a detection threshold value, determining the boundary of the defect by connection and chain codes, and determining the type of the defect according to the distribution of sensors of an internal detector.
Description
Technical field
The invention belongs to input and area of pattern recognition, be specifically related to the defect of pipeline feature extracting method based on single shaft magnetic flux leakage data and device.
Background technology
China is located in Western Pacific bank, submarine surface ground is unstable, subsea pipeline and underwater structure suffer dielectric corrosion, ocean current to rush impact that is naughty and marine accident for a long time simultaneously, sea-bottom oil-gas pipeline easily produces defect and damage, also can there is booster, oil and gas leakage or platform time serious to collapse, cause huge economic loss, also cause marine environmental pollution simultaneously.In order to avoid the generation of the similar accidents such as oil and gas leakage, periodic detection and maintenance should be carried out to in-service pipeline, the latent defect in testing pipes wall.China has carried out oil exploitation at shallow sea area in the nineties in last century, and many subsea pipelines have been on active service more than ten years, detect and repair imperative.
Feature extraction is a concept in computer vision and image procossing, and it refers to and uses computing machine to extract image information, determines whether the point of each image belongs to a characteristics of image.The result of feature extraction is that the point on image is divided into different subsets, and these subsets often belong to isolated point, continuous print curve or continuous print region.
China Petroleum Pipeline company cooperates with certain company external the tentative application having carried out three axle high-definition magnetic leakage detecting devices, this three axles high-definition magnetic leakage detecting device utilizes four three-dimensional Hall elements to replace 4 traditional coil pickoffs, can record the magnetic leakage signal of three discrete axial.Detecting the metal increase defect and three axis signals in data with characteristic feature.By to detection signal analysis and the analysis of excavation the result, find that this signal characteristic is obvious, significantly enhance the accuracy that flaw size is judged, improve accuracy of detection, high with the excavation testing result goodness of fit.This detecting device can well analyze the defect in pipeline, but because the sensor used is many, image data amount is large, also just larger to the capacity requirement of memory module, and because be that device can generate heat in process, therefore because data volume is large, so heating is more serious.Be compared to above-mentioned detecting device, the defect extraction element based on single shaft data is less, also less for memory module capacity requirement.
Summary of the invention
For the deficiencies in the prior art, the present invention proposes based on the defect of pipeline feature extracting method of single shaft magnetic flux leakage data and device, simple to reach structure, only uses single shaft data to carry out feature extraction, only stores characteristic, greatly reduce the object of storage space.
Based on the defect of pipeline feature deriving means of single shaft magnetic flux leakage data, comprise: interior pick-up unit main body, magnet, single shaft Hall element and control module circuit board, described control module circuit board comprises analog switch, voltage follower, low-pass filter, AD conversion module, DSP data processing module, FPGA and defect characteristic storer, interior pick-up unit main body is right cylinder, its one end is provided with the first magnet, its other end is provided with the second magnet, first magnet and the second magnet are torus, and be placed in interior pick-up unit main body, cell body is provided with between the first described magnet and the second magnet, described cell body is torus, this cell body outside surface is provided with groove uniformly, single shaft Hall element is provided with in described groove, the front vertical of described single shaft Hall element is radial in pipeline.
The number of described groove is 10 ~ 40.
The number being provided with single shaft Hall element in described groove is 1 ~ 12.
Adopt defect characteristic storer as data-carrier store, a storage defect characteristic.
Adopt the defect characteristic extracting method based on the defect of pipeline feature deriving means of single shaft magnetic flux leakage data, it is characterized in that: step is as follows:
Step 1, DSP data processing module read the transition single shaft stray field signal data collected from in-pipeline detection device, and carry out process formation leakage field curve by data fusion and interpolation to data;
Step 2, in DSP data processing module, carry out removal noise to the single shaft stray field signal data detected, signal strengthens process;
Step 3, be picture element signal by field signal by Mapping and Converting in DSP data processing module, be converted into gray level image;
Step 4, in DSP data processing module, judge whether there is defective region in current single shaft stray field signal data, namely the value this moment detected and previous sampled value do difference, if difference is greater than setting value, then feature extraction is carried out to defect area, perform step 5, if difference is less than setting value, then perform step 9;
Step 5, in DSP data processing module, filtering is again carried out to pixel image, by calculating setting pixel threshold, by this pixel threshold, two-value process being carried out to image, image being converted to black and white two color image;
Step 6, in DSP data processing module, image to be operated, by using the border of chain code determination single shaft magnetic flux leakage data image deflects;
Step 7, in DSP data processing module according to having shown that the image on defect border and the sampling interval of leakage field curve obtain the length and width of defect, girth, area and the degree of depth respectively;
Step 8, in DSP data processing module, defect characteristic to be classified according to criteria for classification, and saving result;
If step 9, proceed signature analysis in DSP data processing module, get back to step 4 and continue to perform; If do not proceed signature analysis, then terminate.
Advantage of the present invention:
The present invention is based on the defect of pipeline feature extracting method of single shaft magnetic flux leakage data and device have structure simple, only use single shaft data to carry out feature extraction, only store characteristic, advantage that storage space is little.
Accompanying drawing explanation
Fig. 1 is the defect of pipeline feature deriving means structural drawing of an embodiment of the present invention based on single shaft magnetic flux leakage data;
In figure, pick-up unit main body in 1-; 2-first magnet; 3-second magnet; 4-groove; 5-cell body; 6-single shaft Hall element; 7-control module circuit board;
Fig. 2 is an embodiment of the present invention magnetization characteristic figure;
Fig. 3 is an embodiment of the present invention single shaft Hall element schematic diagram in the duct;
In figure, 601-tube wall; 602-iron brush; 603-defect; 604-single shaft Hall element; 605-first magnet; 606-second magnet; 607-yoke;
Fig. 4 is the structural representation of an embodiment of the present invention control module circuit board;
Fig. 5 is an embodiment of the present invention data acquisition circuit schematic diagram;
Fig. 6 is an embodiment of the present invention control module circuit theory diagrams;
Fig. 7 is an embodiment of the present invention Magnetic Flux Leakage Inspecting process flow diagram;
Fig. 8 is an embodiment of the present invention leakage field curve map;
Fig. 9 is an embodiment of the present invention four connected sum eight connectivity figure, and in figure, A figure is four connected graphs, and B figure is eight connectivity figure;
Figure 10 is an embodiment of the present invention defect of pipeline border and areal map, and in figure, A figure is former areal map, and B figure is border and the areal map of four connections, and C figure is border and the areal map of eight connectivity;
Figure 11 is the connectivity diagrams of an embodiment of the present invention chain code, and in figure, A figure is four connection schematic diagram, and B figure is eight connectivity schematic diagram, and C figure is four connection chain code schematic diagram, and D figure is eight connectivity chain code schematic diagram.
Embodiment
Below in conjunction with figure, embodiments of the invention are described further.
Fig. 1 is the defect of pipeline feature deriving means structural drawing of an embodiment of the present invention based on single shaft magnetic flux leakage data, this device is based on the defect of pipeline feature extraction of single shaft magnetic flux leakage data, comprise: interior pick-up unit main body, magnet, single shaft Hall element and control module circuit board, described control module circuit board comprises analog switch, voltage follower, low-pass filter, AD conversion module, DSP data processing module, FPGA and defect characteristic storer, interior pick-up unit main body is right cylinder, its one end is provided with the first magnet, its other end is provided with the second magnet, first magnet and the second magnet are torus, and be placed in interior pick-up unit main body, cell body is provided with between the first described magnet and the second magnet, described cell body is torus, this cell body outside surface is provided with groove uniformly, single shaft Hall element is provided with in described groove, the front vertical of described single shaft Hall element is radial in pipeline.The number of described groove is 10 ~ 40.The number being provided with single shaft Hall element in described groove is 1 ~ 12.
Stray field Producing reason is that the magnetic flux density in flux path changes, the magnetic line of force bends, and the generation of this phenomenon is based upon on ferrimagnet high magnetic permeability characteristic basis, utilizes Hall element to detect stray field and can know that defect exists situation.After ferrimagnet in closed magnetic circuit is magnetized under the effect of driving source, if ferrimagnet is the isotropic medium of uniformly continous, most of magnetic line of force will be constrained on material internal, and material surface does not almost have the magnetic line of force to pass.When inside or the top layer existing defects of material, there is very big-difference in the magnetic permeability of the medium (being generally air) that high magnetic permeability and fault location due to ferromagnetic material are filled.In still unsaturated situation, and the ratio that defect occupies is less, and the remaining continuous part of material still can hold whole magnetic flux, and so magnetic flux passes through in the preferential material less from magnetic resistance, and just the magnetic flux density of material internal becomes large.When nearly saturated magnetization, when the size of defect is larger, the magnetic flux density near defect is difficult to increase, and part magnetic flux can overflow from rejected region, passes through defect ambient air and enters material again, thus forms leakage flux.
Such as: in the area of section be S steel plate on existing defects, the sectional area of defect is Δ S, then the residual area of defect area steel plate is S-Δ S.If the magnetic field intensity of magnetizing field is definite value H, in steel plate, the magnetic induction density at zero defect place is B.Be: Φ=BS be: Φ=B ' * (S-Δ S), namely by the total magnetic flux in steel plate cross section in defect processing strain
the magnetic induction density of fault location increases because of the existence of defect, but because material is closely saturated, can draw from magnetization curve figure: magnetic permeability μ is tending towards declining, and the variation range of Δ B=B '-B is very little.Actual magnetic flux is Φ '=B ' * (S-Δ S) ≈ B* (S-Δ S) < Φ, so some magnetic flux inevitable is leaked in the medium of surrounding from material.According to border magnetic flux continuous print principle, the magnetic flux B of steel plate outside surface
sfor:
in formula, μ
sfor the relative permeability of air, μ is the relative permeability of steel plate, and B is the magnetic induction density in steel plate.Its magnetization characteristic as shown in Figure 1.Fig. 3 is embodiment of the present invention single shaft Hall element schematic diagram in the duct, by the field signal between the probe detection device two ends magnet of single shaft Hall element.
Fig. 4 is the structural representation of embodiment of the present invention control module circuit board, single shaft Hall element selects ss495, in this example, supply voltage is 5VDC, the representative value of supply current when supply voltage is 5VDC is 7.0mA, so low in energy consumption, the way of output is that ratio is linear, can react on positive or negative magnetic field.Cell body in the embodiment of the present invention is evenly distributed with 22 grooves, and is provided with 4 single shaft Hall elements in each groove, each single shaft Hall element front vertical is radial in pipeline, namely uses 88 single shaft Hall elements altogether.Each single shaft Hall element ss495 has 3 pins, and No. 1 is connected 5VDC and in analog respectively with No. 2 pins, and No. 3 pins are output pins, are connected to the input end of analog switch.
The signal that single shaft Hall element collects, by analog switch, enters AD conversion module after voltage follower and low-pass filter.Because AD conversion module requires input is one-channel signal, and single shaft Hall element collects is the field signal of multichannel.Therefore the conversion carrying out from multichannel to single channel to signal by analog switch is needed.
CD4067 selected by analog switch, and CD4067 is digital control analog switch, has low conduction impedance, the feature of low cut-off leakage current and home address decoding.In addition, in whole input reference signal, conducting resistance keeps relative stability.CD4067 is 16 channel switchs, and every a slice CD4067 has these 16 input channels of I0 ~ I15, and have four scale-of-two input end A0 ~ A3 and control end C, any one combined optional of input selects a way switch, as C=1, represents closeall passage.In the embodiment of the present invention, use 6 CD4067, port number can be allowed to be that 96,1st ~ 5 CD4067, the I0 ~ I14 of every sheet connect the output terminal of 15 single shaft Hall element ss495, last passage I15 input end of every sheet is through a resistance eutral grounding, as zero point, prevent the zero point drift that the thermal characteristics of device causes, the I0 ~ I12 of the 6th CD4067 connects 13 single shaft hall sensor output, last three input end I3 ~ I15 link together, and through a resistance eutral grounding.
The output terminal of each analog switch connects a voltage follower, plays the effect of buffering, isolation, raising load capacity.Form voltage follower by operational amplifier TL082 in the present embodiment, the output terminal O of 6 CD4067 connects No. 3 pins of TL082, the i.e. in-phase input end of TL082.
Each voltage follower output terminal connects a RC low-pass filter, the high frequency noise in filtered signal, and No. 1 pin of TL082 connects resistance one end of RC low-pass filter as output pin.
AD sampling module selects the AD7606 of ADI company of the U.S.; AD7606 is 16,6 Channel Synchronous sampling analog to digital converters, built-in analog input clamping protection, second order frequency overlapped-resistable filter, follows the tracks of hold amplifier, 16 Charge scaling SAR ADC, flexibly digital filter, 2.5V reference voltage source, reference voltage buffering and high speed serialization and parallel interface.6 sampling modules are adopted in the embodiment of the present invention, each AD7606 adopts 5V single power supply, electric capacity one end V1 that electric capacity one end of RC low-pass filter is connected to six input end of analog signal V1 ~ V6(the 1st AD sampling module of AD7606 is connected to six input end of analog signal V1 of AD7606, by that analogy, until the 6th AD sampling module), digital signal after analog-converted adopts the 16 bit parallel way of outputs, is held export by the DB0 ~ DB15 of AD7606.
Fig. 6 is embodiment of the present invention control module circuit theory diagrams, and control module adopts FPGA+DSP mode.FPGA is responsible for control AD sampling module, carries out the operations such as feature extraction at DSP by the algorithm routine write.In inventive embodiments, the data volume handled by lower layer signal Preprocessing Algorithm is large, high to the rate request of process, but operating structure is relatively simple, is applicable to FPGA hardware implementing, takes into account speed and dirigibility so simultaneously.The feature of high-rise Processing Algorithm is that handled data volume lower level algorithm is few, but the control structure of algorithm is complicated, is applicable to high by arithmetic speed, that addressing mode is flexible, communication mechanism is powerful dsp chip and realizes.
FPGA adopts the EP3C25F324C8N model of CycloneIII series.Control AD conversion by FPGA, the 16 bit parallel data DB0 ~ DB15 after AD conversion gives the DB0 ~ DB15 in the I/O interface of FPGA.DSP data acquisition module selects the TMS320C6713 of TI company to be master controller, in embodiments of the present invention, DSP is connected with FPGA, SDRAM respectively by EMIF interface (i.e. ED0 ~ ED31), DSP, FPGA and SDRAM be usage data bus ED0 ~ ED31 together, and address bus EA2 ~ EA21.Signal data enters DSP, denoising is carried out by the program downloaded in DSP, image enhaucament, the voltage signal detected by single shaft Hall element is to the conversion of pixel map, and carry out feature extraction and the classification of defects operation of defect, finally defect of pipeline characteristic sum classification of defects situation is saved in storer.
Generating a data width in FPGA inside is 32bit, the degree of depth is the asynchronous FIFO module of 512, buffer memory is exported as data, the data of buffer memory after AD conversion, half-full mark HALF_FULL(and INT4 mouth) be connected to the interrupt INT 4 of TMS320C6713, when FIFO is half-full, DSP reads the data of buffer memory from the I/O port of FPGA by data bus ED0 ~ ED31, after carrying out feature extraction, by data bus ED0 ~ ED31, characteristic is stored in SDRAM again, therefore DQ0 ~ DQ31 pin of EMIF interface ED0 ~ ED31 pin by TMS320C6713 and SDRAM is needed.
Data-carrier store SDRAM selects MT48LC2M32B2TG, and capacity is the SDRAM of 64Mb:x32, MT48LC2M32B2TG is 512Kx32x4banks.Just store characteristic in the present embodiment, so substantially reduce storage space.
Invention herein provides a kind of defect of pipeline feature extracting method based on single shaft magnetic flux leakage data, the method is the single shaft data in the defect and magnetic leakage data obtained when utilizing in-pipeline detection device to patrol and examine in pipeline, its magnetic field branch is analyzed, and extracted by the feature of simple algorithm to defect, and eventually through extracted defect characteristic, defect is classified.Fig. 7 is embodiment of the present invention Magnetic Flux Leakage Inspecting process flow diagram, and the method is carried out as follows:
Step 1, DSP data processing module read the transition single shaft stray field signal data collected from in-pipeline detection device, and carry out process formation leakage field curve by data fusion and interpolation to data;
Fig. 8 is an embodiment of the present invention leakage field curve map, the embodiment of the present invention adopts Data fusion technique, utilize computing machine to the some observation information obtained chronologically, in addition automatic analysis, comprehensive under certain criterion, the information processing technology of carrying out to complete required decision-making and evaluation tasks.The space when embodiment of the present invention adopts the method for interpolation to be used for filling image conversion between pixel.Interpolation continuous function on the basis of discrete data, makes this continuous curve by all given discrete data point.Utilize the method by the value situation of function at limited some place, estimate the approximate value of function at other some places.
Step 2, in DSP data processing module, carry out removal noise to the single shaft stray field signal data detected, signal strengthens process;
Step 3, be picture element signal by field signal by Mapping and Converting in DSP data processing module, be converted into gray level image;
According to the arrangement feature of sensor, obtain the arrangement pitches of every two detecting sensors, pipeline is detected, so detecting device is (d along the sampling interval that pipeline axial is respectively popped one's head in owing to using the pipe leakage internal detector along cylindrical outer surface even circumferential distribution probe
0=arrangement girth/Hall element number); Show that the interval d ' of each sampled point of data is detected on each road according to detecting device fltting speed
0; Cause there is spacing d between the magnetic line of force in the image I in step 3 due to sensor arrangement reason
0, the space when embodiment of the present invention adopts the method for interpolation to be used for filling image conversion between pixel, the new spacing calculating axial magnetic flux leakage data according to the degree of interpolation is d
1if insert n group new data by interpolation algorithm between the Monitoring Data of every two groups of Hall elements, so new spacing
Step 4, in DSP data processing module, judge whether there is defective region in current single shaft stray field signal data, namely the value this moment detected and previous sampled value do difference, if difference is greater than setting value 25.6, then feature extraction is carried out to defect area, perform step 5, if difference is less than setting value 25.6, then perform step 9; Setting value wherein according to the situation.
Step 5, the process of black and white two value filtering is carried out to the image in step 4: set a pixel threshold P
0if, original pixel P<P
0, then the pixel at this place is set to 0; If pixel P>P originally
0, then the pixel at this place is set to 1, is designated as image II.
If the gray-scale value of piece image is 0 ~ m level, the pixel of gray-scale value i is n
i, be then divided into two groups of c with k
0={ 0 ~ k} and c
1={ k+1 ~ m}, the variance between two groups is
Wherein:
the mean value of whole gradation of image;
average gray when be elected threshold value of going being k;
c
0the probability produced;
μ
0, μ
1c
0, c
1selected threshold is the average gray that k is;
for group c
0, c
1the probability produced.
Between 0 ~ m, change k, k(when asking above formula to be maximal value works as k=k
*time), namely ask δ
2k corresponding to (k)
*value is threshold value, δ
2k () is exactly threshold selection function.
Step 6, in DSP data processing module, image to be operated, by using the border of chain code determination single shaft magnetic flux leakage data image deflects;
Defect border is determined: in order to point and the frontier point of defined range inside, needs to consider the neighbouring relations between each pixel after filtering process again, and these relations can describe by connection rule.The connection method of usual definition has two kinds: four connected sum eight connectivity, and Fig. 9 is the embodiment of the present invention four connected sum eight connectivity figure.Four are communicated with the pixel connected relation only analyzing direct neighbor point, eight connectivity analysis be connected relation around target pixel points between eight pixels.Border can define with being communicated with of this two type with region, and they are complementary, namely, if border is four connections, so region is exactly eight connectivity, and Figure 10 is embodiment of the present invention defect of pipeline border and areal map, figure Oxford gray represents border, light grey expression region.In order to express profile, can storage figure as the coordinate of pixel sequence, also only can store the relation between contiguous pixels.Suppose there is a border completely, i.e. one group of tie point, from a pixel, find next pixel in the direction of the clock.Namely next pixel is on certain main assigned direction in consecutive point.Then, only need the numeric string in the continuous direction of specifying next pixel just to define chain code together.
Figure 11 is the connectivity diagrams of embodiment of the present invention chain code, and in figure, A figure is four connection schematic diagram, and B figure is eight connectivity schematic diagram, and C figure is four connection chain code schematic diagram, and D figure is eight connectivity chain code schematic diagram.As schemed in D, the pixel on defect border being traveled through, finding out the rightmost point of the first row, being labeled as starting point P0, being set to and being labeled.Determination relation according to eight connectivity chain code carries out picture element scan to adjacent 8 pixels of a P0 in the direction of the clock, scanning first pixel value is the consecutive point that the point of black is labeled as P0, be the point on P0 direction 3 shown in figure, be designated as P1, some P1 is set to simultaneously and is labeled.Assign P1 as starting point again, in the direction of the clock to the scanning one by one of adjacent 8 points of a P1, the some P2 obtained on outgoing direction 4 is first black color dots, is designated as another frontier point that P1 is adjacent, is set to by a P2 simultaneously and is labeled.By that analogy, till some P0 is confirmed as last adjacent frontier point, just defect border can be determined.
Step 7, in DSP data processing module according to having shown that the image on defect border and the sampling interval of leakage field curve obtain the length and width of defect, girth, area and the degree of depth respectively;
By the boundary curve that step 6 obtains, length axial length, girth, area and the degree of depth of defect can be obtained according to the interval of the movement velocity of internal detector, sampling rate and axial data.
The major axis of defect gets the maximal value (L1 as in D figure in Figure 11) of the spacing of any two pixels on border;
Minor axis perpendicular to long axis direction, computing method similar (L2 as in D figure in Figure 11);
Defect girth is exactly the length of all pixels around defect, represents by the distance sum between two between pixel adjacent on Defect Edge.Because the length of chain code is fixing, be d ' in 0,4 directional chain-code length
0; Be d in 2,6 directional chain-code length
1; 1,3,5,7 directional chain-code length are
defect Edge is traveled through, scans from first pixel be labeled, then add up the d ' in eight connectivity region between each edge pixel
0, d
1with the chain code number of d, then be multiplied by length value and just obtain perimeter value.
Defect area illustrates the size of defect area, and the number that can pass through statistical shortcomings inside (comprising defect border) all pixels is tried to achieve.
Depth of defect illustrates the thickness that pipeline is corroded, and can obtain stray field signal degree of depth D by the difference calculating defect magnetic flux leakage field maximum intensity and defect magnetic flux leakage field minimum strength in defect area.
Step 8, step 8, in DSP data processing module, defect characteristic to be classified (criteria for classification is determined in advance according to field condition) according to criteria for classification, and saving result;
Step 9, judge whether to proceed signature analysis in DSP data processing module, if continue, get back to step 4 and continue to perform; If do not continue, then terminate.
Claims (1)
1. the defect of pipeline feature extracting method based on single shaft magnetic flux leakage data, adopt the defect of pipeline feature deriving means based on single shaft magnetic flux leakage data, described device comprises interior pick-up unit main body, first magnet, second magnet, single shaft Hall element and control module circuit board, described interior pick-up unit main body is right cylinder, its one end is provided with the first magnet, its other end is provided with the second magnet, first magnet and the second magnet are torus, and be placed in interior pick-up unit main body, cell body is provided with between the first described magnet and the second magnet, described cell body is torus, this cell body outside surface is provided with groove uniformly, single shaft Hall element is provided with in described groove, the front vertical of described single shaft Hall element is radial in pipeline, described control module circuit board comprises analog switch, voltage follower, low-pass filter, AD conversion module, DSP data processing module, FPGA and defect characteristic storer, it is characterized in that: described method comprises the steps:
Step 1, DSP data processing module read the transition single shaft stray field signal data collected from described defect of pipeline feature deriving means, and carry out process formation leakage field curve by data fusion and interpolation to data;
Step 2, in DSP data processing module to the single shaft stray field signal data detected carry out removal noise, signal strengthen process;
Step 3, be picture element signal by field signal by Mapping and Converting in DSP data processing module, be converted into gray level image;
Step 4, in DSP data processing module, judge whether there is defective region in current single shaft stray field signal data, namely the value this moment detected and previous sampled value do difference, if difference is greater than setting value, then feature extraction is carried out to defect area, perform step 5, if difference is less than setting value, then perform step 9;
Step 5, in DSP data processing module, filtering is again carried out to pixel image, by calculating setting pixel threshold, by this pixel threshold, two-value process being carried out to image, image being converted to black and white two color image;
Step 6, in DSP data processing module, described black and white two color image to be operated, by using the border of chain code determination single shaft magnetic flux leakage data image deflects;
Step 7, in DSP data processing module according to having shown that the image on defect border and the sampling interval of leakage field curve obtain the length and width of defect, girth, area and the degree of depth respectively;
Step 8, in DSP data processing module, defect characteristic to be classified according to criteria for classification, and saving result;
If step 9, proceed signature analysis in DSP data processing module, get back to step 4 and continue to perform; If do not proceed signature analysis, then terminate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210177825.1A CN102706955B (en) | 2012-05-31 | 2012-05-31 | Pipeline defect characteristic extraction method and device based on uniaxial magnetic leakage data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210177825.1A CN102706955B (en) | 2012-05-31 | 2012-05-31 | Pipeline defect characteristic extraction method and device based on uniaxial magnetic leakage data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102706955A CN102706955A (en) | 2012-10-03 |
| CN102706955B true CN102706955B (en) | 2015-04-22 |
Family
ID=46899894
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210177825.1A Expired - Fee Related CN102706955B (en) | 2012-05-31 | 2012-05-31 | Pipeline defect characteristic extraction method and device based on uniaxial magnetic leakage data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102706955B (en) |
Families Citing this family (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103343885B (en) * | 2013-06-20 | 2015-10-14 | 西南石油大学 | Pipeline Magnetic Flux Leakage Inspection online data compression method |
| AU2015264529B2 (en) * | 2014-05-18 | 2019-07-04 | The Charles Stark Draper Laboratory, Inc. | System and method of measuring defects in ferromagnetic materials |
| CN104063588B (en) * | 2014-06-12 | 2017-03-22 | 东北大学 | Multi-source data fusion-based method for predicting pipeline corrosion defect size |
| CN104075735B (en) * | 2014-07-03 | 2017-02-15 | 湖北航天技术研究院总体设计所 | Self diagnosis method and self diagnosis device for inertia measurement device |
| US10454174B2 (en) | 2016-05-10 | 2019-10-22 | Novatel Inc. | Stacked patch antennas using dielectric substrates with patterned cavities |
| CN106324687B (en) * | 2016-10-29 | 2019-05-14 | 西南石油大学 | A kind of buried irony pipeline detection and accurate positioning method |
| CN106442707A (en) * | 2016-12-14 | 2017-02-22 | 中国计量大学 | Metal pipeline defect detecting device based on low-frequency electromagnetism |
| CN106404893B (en) * | 2016-12-16 | 2019-05-03 | 北京华航无线电测量研究所 | A kind of axial direction pipeline magnetic flux leakage defect automatic signal detection method |
| US10712292B2 (en) * | 2018-03-29 | 2020-07-14 | The Boeing Company | Backscatter x-ray inspection system for pipes |
| CN108982522B (en) * | 2018-08-09 | 2022-04-26 | 北京百度网讯科技有限公司 | Method and apparatus for detecting pipe defects |
| CN109324112B (en) * | 2018-10-09 | 2020-11-10 | 河海大学 | Gate detection robot based on giant magnetoresistance element and detection method |
| CN109697714B (en) * | 2018-11-26 | 2021-08-17 | 联想(北京)有限公司 | Information detection method, equipment and computer storage medium |
| CN109765292B (en) * | 2019-02-18 | 2024-03-26 | 西南石油大学 | Accurate positioning method for pipeline defects |
| CN110006338B (en) * | 2019-04-28 | 2020-11-06 | 哈尔滨工业大学（深圳） | Method for detecting damage area of steel wire rope |
| CN110307796A (en) * | 2019-06-27 | 2019-10-08 | 南京理工大学 | A kind of FBG strain gauge means of high dynamic response |
| CN112228696A (en) * | 2020-11-04 | 2021-01-15 | 杭州申昊科技股份有限公司 | Air bag type pipeline robot |
| CN112228697A (en) * | 2020-11-04 | 2021-01-15 | 杭州申昊科技股份有限公司 | Crawler-type pipeline robot |
| CN114354741B (en) * | 2022-03-21 | 2022-06-17 | 广东海洋大学 | Pipeline defect detection method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5864232A (en) * | 1996-08-22 | 1999-01-26 | Pipetronix, Ltd. | Magnetic flux pipe inspection apparatus for analyzing anomalies in a pipeline wall |
| US6023986A (en) * | 1997-03-24 | 2000-02-15 | Bj Services Company | Magnetic flux leakage inspection tool for pipelines |
| CN1828219A (en) * | 2006-04-06 | 2006-09-06 | 上海交通大学 | Intelligent detector for submarine pipeline |
| CN1942762A (en) * | 2004-04-15 | 2007-04-04 | Tdw特拉华有限公司 | ID-OD discrimination sensor concept for a magnetic flux leakage inspection tool |
| CN101685099A (en) * | 2008-09-27 | 2010-03-31 | 中国石油化工股份有限公司 | Probe bracket for in-pipeline detection device |
-
2012
- 2012-05-31 CN CN201210177825.1A patent/CN102706955B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5864232A (en) * | 1996-08-22 | 1999-01-26 | Pipetronix, Ltd. | Magnetic flux pipe inspection apparatus for analyzing anomalies in a pipeline wall |
| US6023986A (en) * | 1997-03-24 | 2000-02-15 | Bj Services Company | Magnetic flux leakage inspection tool for pipelines |
| CN1942762A (en) * | 2004-04-15 | 2007-04-04 | Tdw特拉华有限公司 | ID-OD discrimination sensor concept for a magnetic flux leakage inspection tool |
| CN1828219A (en) * | 2006-04-06 | 2006-09-06 | 上海交通大学 | Intelligent detector for submarine pipeline |
| CN101685099A (en) * | 2008-09-27 | 2010-03-31 | 中国石油化工股份有限公司 | Probe bracket for in-pipeline detection device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102706955A (en) | 2012-10-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102706955B (en) | Pipeline defect characteristic extraction method and device based on uniaxial magnetic leakage data | |
| CN201600164U (en) | Video water-level gauge | |
| CN102798660B (en) | Based on inner and outer walls of pipeline defect detecting device and the method for three axle leakage fields and current vortex | |
| CN110057904B (en) | Method and device for quantitatively detecting defects of moving metal component | |
| CN106643965B (en) | Method for accurately identifying liquid level by utilizing template matching | |
| CN102192953A (en) | Low-power consumption intelligent three-dimensional magnetic leakage detecting probe | |
| CN103322928B (en) | Based on similar model displacement field measuring system and the method for the Mesh Point Battle | |
| WO2021189717A1 (en) | Defect type evaluation method based on fusion of eddy current testing signal and magnetic flux leakage testing signal | |
| CN110689512B (en) | Method for quickly splicing and fusing annular images of panoramic video in hole into image | |
| CN102654479B (en) | Metal pipeline corrosion defect full-digitalization three-dimensional magnetic leakage signal acquisition system | |
| CN102592284A (en) | Method for transforming part surface appearance three-dimensional high-density point cloud data into grayscale image | |
| CN105548350A (en) | Pulsed eddy-current defect detection imaging system based on filleted-corner rectangular array probe | |
| CN103927528A (en) | Coal and rock recognition method based on close neighborhood pixel gray level joint distribution characteristics | |
| CN201965115U (en) | Metal pipeline corrosion defect full-digitalization three-dimensional magnetic leakage signal acquisition system | |
| CN104268940A (en) | MEMS structure reconstruction and detection method based on CT scanned images | |
| CN110220968A (en) | A kind of three axis defect and magnetic leakage detection devices for ferrimagnet | |
| CN103065296B (en) | High-resolution remote sensing image residential area extraction method based on edge feature | |
| US11948284B2 (en) | 3D modeling method for pore-filling hydrate sediment based on CT image | |
| CN205940716U (en) | Liquid level sensor | |
| CN101957458A (en) | Method for processing ultralow frequency data | |
| CN203178236U (en) | Novel magnetic flux leakage test sensor | |
| US20220230326A1 (en) | 3d modeling method for cementing hydrate sediment based on ct image | |
| CN104167046A (en) | Magnetic graph anti-fake method and system | |
| CN108613873B (en) | Beam bridge structure damage positioning method based on joint phase space of double-sensor information | |
| Meng et al. | Research on reading recognition method of pointer meter based on magnifying the virtual circle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20150422 |