CN105938620B - A kind of small-bore pipe inside weld Surface Defect Recognition device - Google Patents
A kind of small-bore pipe inside weld Surface Defect Recognition device Download PDFInfo
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Abstract
A kind of small-bore pipe inside weld Surface Defect Recognition device, the device include computer, industrial camera, adapter, industrial endoscope, Mobile portable formula light source, endoscope stationary fixture and pipe fitting to be measured.Industrial camera is connected with computer, and adapter one end is connected with industrial camera, and the other end is connected with industrial endoscope, can be used for the acquisition of weld image.Mobile portable formula light source is connected with industrial endoscope, for providing the illumination in pipe.Endoscope stationary fixture is connected with endoscope, positions for the centralized positioning of endoscope, up and down adjustment and 360 ° rotate.The recognition detection of test tube part internal weld seams surface defect is treated in completion.
Description
Technical field
The present invention relates to a kind of detection device of weld seam welding defect, especially a kind of small-bore pipe inside weld surface defect
Identification device.The device is suitable for the internal weld seams welding defect at the transducing devices osculum pipelines such as pipeline, boiler and header
Detection identification, belongs to field of non destructive testing.
Background technique
With the development of China's industrial sector, the transducing devices such as boiler, pipeline and header are as the pass for bearing high temperature and pressure
Key member, manufacturing quality are more and more paid close attention to by people.Since transducing devices should be born caused by being acted on as medium
Internal pressure, will also bear the stress as caused by internal-external temperature difference, and failure damage easily appears in interior welds region.Therefore urgent
It needs to develop a kind of effective internal weld seams weld defects detection method, is reliably transported for equipment such as pipeline, boiler and headers
Row provides technical guarantee.
Currently, lacking effective detection means to small-bore pipe internal weld seams surface welding quality, artificial detection is mostly used
Method.Due to the weld seam moved on testing staff's long focus screen, it is be easy to cause eye strain, causes missing inspection, and examine
The quality of personnel, the difference of skills and experience are tested, inevitably has deviation to the assurance of quality control standards (QCS), testing result is caused to be examined
The Subjective Factors of survey personnel are larger, are difficult to make defect the judgement of accurate quantitative analysis.In order to further increase osculum pipeline
The welding quality on inside weld surface improves the testing conditions of testing staff, and there is an urgent need to a kind of osculum pipeline inside weld surfaces to lack
Sunken automatic identifying method.
In recent years, for the identifying system for building weld defect, domestic and foreign scholars have done a large amount of research and development,
[the Assessmentofwelding defects for gas pipeline radiographs using such as Shafeek
Computer vision [J] .NDT&E International, 2004,37 (6): 301-307] development platform develop it is a
The welding defect identifying system of entitled AutoWDI.The system acquires weld seam egative film, recycles by building x-ray bombardment system
Obtained egative film is analyzed and processed by relevant image processing techniques, to obtain the relevant parameter of welding defect, and according to ginseng
Number information identifies defect.
For deficiency existing for current osculum pipeline inside weld surface defects detection, this patent proposes a kind of using in industry
The face of weld defect device of sight glass and industrial camera, method used by the present apparatus are based on gray level co-occurrence matrixes and BP nerve
The defects of network class identification judges weldering, overlap and burn-through type.Face of weld indent is realized based on technique of binocular stereoscopic vision
The identification of defect.
Summary of the invention
The purpose of the present invention is to provide a kind of small-bore pipe inside weld Surface Defect Recognition devices, are based particularly on gray scale
Co-occurrence matrix and BP neural network identification is broken, overlap and the defects of burn-through, identifies weld seam table based on technique of binocular stereoscopic vision
The identification device of concave defect in face.By the acquisition to small-bore pipe inside weld surface image, for being broken, overlap and burn-through etc.
Defect type obtains the characteristic parameter for containing different poor weld surfaces based on gray level co-occurrence matrixes, and classifier uses BP nerve net
Network butt-welding fitting workpiece face of weld defect is trained, classifies and identification.Binocular stereo vision is based on for concave surface defect
Technology obtains weld seam indent degree, realizes the identification of concave defect in face of weld.
It is proposed by the present invention based on gray level co-occurrence matrixes and BP neural network and based on the weldering of technique of binocular stereoscopic vision
The detection method of seam surface defect, basic principle are:
The weld seam different to surface welding quality carries out Image Acquisition, and the texture in weld seam usually embodies face of weld
Texture features information, the i.e. uneven characteristic of body surface.The good weld seam of welding quality is in scalelike mark shape, and texture is more
Uniformly.And welding quality it is poor weld seam its surface texture it is more mixed and disorderly, variation is irregular, and the textural characteristics of different defects
Also different.And gray level co-occurrence matrixes can be good at describing irregular texture, distinguish small morphological differences, it can be preferably
Extraction face of weld effective characteristic parameters.Its gray level co-occurrence matrixes is extracted from weld image, obtains face of weld
Characteristic parameter realizes the mathematical character of face of weld textural characteristics.
G (i, j) indicates gray level co-occurrence matrixes, and in order to simplify operation, matrix element is often indicated with probability value, i.e., by element G
(i, j) obtains the normalized value that each element is both less than 1 divided by the sum of each element S
(1) energy (ASM)
Energy reflects the uniformity coefficient and texture fineness degree of weld image intensity profile.If whole numbers in co-occurrence matrix
Value is equal, then energy value is smaller, conversely, energy value is larger if matrix numerical value differs.I.e. energy value can be shown that weld seam texture
The uniformity of variation and regular degree.
(2) contrast (Contrast)
The contrast of weld image can be understood as the clarity of weld image, i.e. weld seam clean mark degree.In weld seam
In image, the groove of texture is deeper, then its contrast C ON is bigger.
(3) correlation (Correlation)
Wherein:
Correlation has reacted the consistency of weld image texture.It is horizontal if there is horizontal direction texture in weld image
The COR of direction matrix is greater than the COR value of remaining direction matrix.Correlation metrizable spaces gray level co-occurrence matrixes element is in ranks side
Upward similarity degree, value size reflect the correlation of local gray level in weld image.When matrix element value homogeneous phase etc.
When, correlation is big, otherwise correlation is smaller.
(4) homogeney (Homogeneity)
The size of the mensurable weld image texture localized variation amount of homogeney.If in certain region the variable quantity of image texture compared with
Small, then the homogeney in the region is larger.
(5) entropy (entropy)
Entropy can reflect information content possessed by weld image, indicate the non-uniform degree of texture or complicated journey in weld image
Degree.
(6) variance (variance)
Wherein u is the mean value of gray level co-occurrence matrixes each element.
This method carries out Classification and Identification to face of weld defect image using BP neural network, and BP network can learn and store
A large amount of input-output mode map relationship.Its learning rules are constantly to be adjusted using steepest descent method by backpropagation
The weight and threshold value of whole network keep the error sum of squares of network minimum.BP neural network model topology structure includes input layer, hidden
Containing layer and output layer.
BP algorithm is made of two processes of the forward calculation (forward-propagating) of data flow and the backpropagation of error signal.Just
To when propagating, the direction of propagation of data flow is again to output layer from input layer to hidden layer, and the state of every layer of neuron only influences
Next layer of neuron.If cannot get desired output, the backpropagation process of turning error signal, error signal in output layer
The direction of propagation be that output layer to hidden layer arrives input layer again.Alternately by the two processes, it is held in weight vector space
Row error function gradient decline strategy, one group of weight vector of dynamic iterative search make network error function reach minimum value, thus complete
At information extraction and Memory Process.
If the input layer of BP neural network has n node, hidden layer has q neuron, and output layer has m neuron, defeated
Entering the weight between layer and hidden layer is vki, the weight between hidden layer and output layer is wjk, the transmission function of hidden layer is f1
(), the transmission function of output layer are f2(·)。
The output of hidden layer node is (threshold value is written in sum term):
Export the output of node layer are as follows:
By formula (11) and formula (12), BP neural network just completes n-dimensional space vector (input layer) to m-dimensional space vector
The approximate mapping of (output layer).
Error function, that is, objective function sets P learning sample (x of input1x2…xp), p-th of sample obtains after inputting network
Output isThe error for defining p-th of sample is Ep, the mean square error of P sample is E.In order to avoid repeatedly
Generation number increases, and arithmetic speed slows down, and guarantees that the error of each sample is reduced, therefore choose mean square error as objective function.
Mean square error:
Network global error E is calculated according to formula (13), and compared with preset anticipation error, whether judges network error
It meets the requirements.When error reaches the precision of anticipation error or study number is greater than the number of iterations of setting, then e-learning knot
Beam；Otherwise, next learning sample and corresponding desired output are chosen, back to the net input value of neuronCarry out data forward-propagating, into next round learn, until meet error precision or
Learn to terminate learning process when number.Finally show the recognition result of weld seam welding quality.
The present invention completes the information extraction of weld seam hollow depth using binocular vision technology.Zhang Zhengyou calibration method pair is used first
The internal reference of camera is demarcated, and determines the focal length of camera；Stereo matching is carried out to image, disparity map is obtained, with curve matching
Method disparity map is refined；Finally use principle of triangulationWherein f is focal length, and b is parallax range, d
For parallax value, three-dimensional information building is completed, chooses at weld edge at 3 points and calculates three selected weld edge points extremely
The distance of camera is averaged the distance l for finding out weld edge to camera1, chosen a bit in Weld pipe mill, seek Weld pipe mill position
It sets to the distance l of camera2, l1And l2Difference be that can determine whether the hollow depth of weld seam.
Technical scheme is as follows:
Device of the present invention referring to Fig. 1, including computer 1, industrial camera 2, adapter 3, industrial endoscope 4,
Mobile portable formula light source 5, endoscope stationary fixture 6 and osculum pipeline 7 to be measured.Industrial camera 2 is connected with computer 1, adapter 3
One end is connected with industrial camera 2, and the other end is connected with industrial endoscope 4, computer 1, industrial camera 2, adapter 3, industry in
Sight glass 4 forms acquisition unit and is acquired to weld image.Mobile portable formula light source 5 is connected with industrial endoscope 4, for providing
7 intraoral illumination of osculum pipeline to be measured.Endoscope stationary fixture 6 is connected with industrial endoscope 4, and the center for industrial endoscope 4 is fixed
Position, up and down adjustment positioning and 360 ° of rotations.Complete the recognition detection to 7 internal weld seams surface defect of small orifice road to be measured.
Endoscope stationary fixture 6 include base platform 8, clamp 9, lead screw 10, endoscope fixed card slot 11, ball bearing 12,
Movable bearing support 13, connecting rod 14；Clamp 9 is mounted on 8 surface of base platform, lead screw 10 is fixed among clamp 9, endoscope is solid
Determine card slot 11 to be mounted on lead screw 10；It is matched between the centre of base platform 8 and movable bearing support 13 by ball bearing 12, activity
The bottom of support 13 is connect with connecting rod 14.
The invention has the following advantages that (1), which substitutes Traditional x-ray with endoscope, acquires face of weld image, traditional X is avoided
Radiation injury of the ray to human body.(2) method based on gray level co-occurrence matrixes and BP neural network is used, can identify small orifice
Diameter inside weld welding quality situation avoids causing missing inspection because of artificial detection fatigue.
Detailed description of the invention
Fig. 1 detection device system diagram；
Fig. 2 endoscope stationary fixture schematic diagram；
Fig. 3 this inside weld Surface Defect Recognition flow chart
Fig. 4 (a)-Fig. 4 (e) training sample image schematic diagram；
Fig. 5 welding quality good sample characteristic parameter schematic table；
Fig. 6 is broken sample characteristics parameter schematic table；
Fig. 7 hole sample characteristics parameter schematic table；
Fig. 8 is partially welded sample characteristics parameter schematic table；
Fig. 9 overlap sample characteristics parameter schematic table；
Figure 10 BP neural network exports result table；
The weld image of the left and right position Figure 11 (a)-Figure 11 (b) acquisition；
Figure 12 disparity map；
In Fig. 1: 1, computer, 2, industrial camera, 3, adapter, 4, industrial endoscope, 5, Mobile portable formula light source, 6, interior
Sight glass stationary fixture, 7, osculum pipeline to be measured.
In Fig. 2: 8, base platform, 9, clamp, 10, lead screw, 11, endoscope fixed card slot, 12, ball bearing, 13, activity branch
Seat, 14, connecting rod.
Specific embodiment
Below with reference to specific experiment, the invention will be further described:
Step 1: experimental system is built: installing pilot system according to the detection device system diagram of Fig. 1, system includes calculating
Machine 1, volume are 29mm × 29mm × 57mm industrial camera 2, F35 optics adapter 3, diameter are 8mm industry rigid endoscope
4, Mobile portable formula light source 5 and endoscope stationary fixture 6.Industrial camera 2 is connected with computer 1,3 one end of adapter and industrial phase
Machine 2 is connected, and the other end is connected with industrial endoscope 4, can be used for the acquisition of weld image.In Mobile portable formula light source 5 and industry
Sight glass 4 is connected, for providing the illumination in pipe.Endoscope stationary fixture 6 is connected with endoscope 4, and the center for endoscope is fixed
Position and up and down adjustment positioning, detect the osculum pipeline that internal diameter is 30mm.
Step 2: capturing sample image: choose first 5 class different surfaces welding qualities image (it is good, hole, be partially welded,
Overlap and be broken), totally 5 × 50 width images are as training sample, (different welding quality face of weld images such as Fig. 4 of acquisition
(a) shown in-Fig. 4 (e)) gray level co-occurrence matrixes are based on, extract energy, the contrast, correlation of different welding quality faces of weld
The characteristic parameters such as property, homogeney, entropy and variance, and characteristic parameter is normalized in the characteristic parameter of extraction.(difference weldering
The characteristic parameter of quality face of weld is connect as shown in Fig. 5 to Fig. 9).
Step 3: the foundation of BP neural network: training sample is input in neural network, and the first of neural network is arranged
Initial value parameter, wherein input layer is set as 24, and output layer is set as 5, and hidden layer neuron is set as 20.Hidden layer transmission function
Logsig transmission function is selected, output layer transmission function selects logsig transmission function, and training algorithm selects gradient decline adaptive
Answer learning training function.Maximum frequency of training is set as 3000 times, training precision 0.01, learning rate 0.01 shows training
Between be divided into 50.
Step 4: the identification of face of weld welding quality: 40 width images of acquisition carry out Classification and Identification, are based on gray scale symbiosis square
Battle array, extracts the characteristic parameters such as energy, contrast, correlation, homogeney, entropy and variance of acquisition image, and by the feature of extraction
Characteristic parameter is normalized in parameter.Acquired image pass through neural network recognization, calculate test sample with it is all kinds of
The matching degree of different surfaces welding quality training sample is chosen and 5 class different surfaces welding quality test sample matching degree highests
One kind, identify the surface welding quality of test sample.The general classification recognition correct rate of the method is 92.5% (BP nerve
The results are shown in Figure 10 for network output)
Step 5: weld seam hollow depth detection；Zhang Zhengyou calibration method is used to calibrate the focal length of camera as 30mm.To preliminary
It is judged as that the good pipeline of welding quality is detected；The reflective lesser station acquisition piece image of welded seam area is chosen (as schemed
Shown in 11 (a)), using motor drive, clamp 2 in mobile Fig. 2, moving distance 2mm, the second width image of acquisition is (as schemed
Shown in 11 (b)).Stereo matching is carried out to image and obtains initial parallax figure, then using curve matching refinement disparity map (such as Figure 12 institute
Show), utilize principle of triangulationComplete three-dimensional information building.3 weld edge points are chosen in central area to make even
The distance for obtaining weld edge to camera is 6.6758mm, and taking the distance of Weld pipe mill point is 6.0022mm, the two range difference
0.6736mm can be approximately that Weld pipe mill region is relative height differential with weld edge region.
It is a typical case of the invention above, it is of the invention using without being limited thereto.
Claims (1)
1. the inspection of the face of weld defect based on gray level co-occurrence matrixes and BP neural network and based on technique of binocular stereoscopic vision
Survey method, it is characterised in that:
Step 1: experimental system is built: installation experimental system, experimental system include computer, volume be 29mm × 29mm ×
Industrial camera, F35 optics adapter, the diameter of 57mm is that 8mm industry rigid endoscope, Mobile portable formula light source and endoscope are solid
Clamp tool；Industrial camera is connected with computer, and adapter one end is connected with industrial camera, and the other end is connected with industrial endoscope,
It can be used for the acquisition of weld image；Mobile portable formula light source is connected with industrial endoscope, for providing the illumination in pipe；Endoscope
Stationary fixture is connected with endoscope, centralized positioning and up and down adjustment positioning for endoscope, the osculum for being 30mm to internal diameter
Pipeline is detected；
Step 2: capturing sample image: choosing the image of 5 class different surfaces welding qualities first, and totally 5 × 50 width images are as instruction
Practice sample, is based on gray level co-occurrence matrixes, extracts energy, the contrast, correlation, homogeneity of different welding quality faces of weld
Property, entropy and Variance feature parameter, and characteristic parameter is normalized in the characteristic parameter of extraction；
Step 3: the foundation of BP neural network: training sample is input in neural network, and the initial value of neural network is arranged
Parameter, wherein input layer is set as 24, and output layer is set as 5, and hidden layer neuron is set as 20；The selection of hidden layer transmission function
Logsig transmission function, output layer transmission function select logsig transmission function, and training algorithm selects gradient decline is adaptive to learn
Practise training function；Maximum frequency of training is set as 3000 times, training precision 0.01, learning rate 0.01 shows training interval
It is 50；
Step 4: the identification of face of weld welding quality: 40 width images of acquisition carry out Classification and Identification, are based on gray level co-occurrence matrixes, mention
Energy, contrast, correlation, homogeney, entropy and the Variance feature parameter of acquisition image are taken, and the characteristic parameter of extraction is carried out
Normalized characteristic parameter；Acquired image passes through neural network recognization, calculates test sample and all kinds of different surfaces
The matching degree of welding quality training sample is chosen and 5 highest one kind of class different surfaces welding quality test sample matching degree, knowledge
Not Chu test sample surface welding quality；
Step 5: the detection of weld seam hollow depth: Zhang Zhengyou calibration method is used to calibrate the focal length of camera as 30mm；To preliminary judgement
It is detected for the good pipeline of welding quality；The reflective lesser station acquisition piece image of welded seam area is chosen, utilization is electronic
Machine driving, mobile clamp, moving distance 2mm acquire the second width image；Stereo matching is carried out to image and obtains initial parallax
Figure, then disparity map is refined using curve matching, utilize principle of triangulationThree-dimensional information building is completed, f is focal length,
B is parallax range, and d is parallax value；Central area choose 3 weld edge points take average acquisition weld edge to camera away from
It is 6.0022mm from the distance for for 6.6758mm, taking Weld pipe mill point, the two range difference 0.6736mm is approximately Weld pipe mill area
Domain is the relative height differential with weld edge region.
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