CN112116583B - SEM image processing-based insulation paperboard aging discrimination inspection method - Google Patents
SEM image processing-based insulation paperboard aging discrimination inspection method Download PDFInfo
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Abstract
The invention discloses an insulation board aging discrimination and inspection method based on SEM image processing, which comprises the following steps: acquiring a front SEM image and a cross-section SEM image of the insulating paperboard sample by using a scanning electron microscope; image preprocessing is carried out by utilizing image enhancement, image denoising and segmentation methods; extracting a fiber edge image; calculating fiber diameter, section porosity and roughness based on the fiber edge image; reconstructing a three-dimensional model of the fiber by utilizing fiber edge images extracted from continuous cross-section SEM images; and analyzing the microscopic morphological defects of the fiber, and judging the aging degree of the insulating paperboard by combining the test criteria of the fiber diameter, the section porosity and the roughness and the three-dimensional model judgment. According to the insulation paperboard aging discrimination inspection method based on SEM image processing, provided by the invention, the microstructure of the SEM image of the insulation paperboard is analyzed by utilizing an image processing technology, so that data support is provided for prolonging the service life of the insulation paperboard and manufacturing a novel insulation paperboard.
Description
Technical Field
The invention relates to the technical field of insulation paperboard aging detection, in particular to an insulation paperboard aging judgment and inspection method based on SEM image processing.
Background
Insulation board is a solid organic insulation material widely used in power transformers and plays a decisive role in the insulation properties of the transformer. However, in the long-term operation process of the transformer, the insulating paper board is gradually deteriorated under the action of various complex operation environments, so that the insulating performance is gradually reduced. Studies have shown that the main component of the insulating board, cellulose, undergoes "depolymerization" and "elimination" reactions under the action of complex environments, which produce large amounts of derivatives, while vicious circle, causing other more complex chemical reactions that continuously change the fiber structure, thus causing the aging of the insulating paper. Through carrying out ageing tests to the insulating paper board, the microscopic characteristics of the paper board under different ageing degrees are checked, and the ageing mechanism is analyzed for researching the ageing rule of the insulating paper board, so that the service life of the insulating paper board is prolonged, and the novel insulating paper board is further manufactured.
In the aspect of judging the aging state of the oil-immersed insulating paper, a method for determining the aging rate of the oil-immersed insulating paper by measuring the Tensile Strength (TS) of the insulating paper is mainly adopted, and when the TS is reduced to half of an initial value, the insulating oil paper is generally considered to enter a later life, and secondly, the polymerization degree is one of the parameter indexes of aging. In recent years, as the precision of observation equipment increases, the magnification increases, and equipment such as FT-IR and UV-Vis, AFM, SEM is used in the field of research on the aging state of insulating oilpaper. The Scanning Electron Microscope (SEM) is widely used in the aspect of the study of the aging mechanism of the paperboard because the microscopic morphology of the surface of the paperboard can be visually analyzed and the fiber damage process can be visually analyzed, but by observing the SEM image, only a direct observation result can be obtained, further deep statistical information can not be obtained, and clear data are needed for accurately judging the aging degree of the insulating paperboard.
Currently, there are two main analytical approaches to SEM image processing: one is based on sophisticated image processing software, such as Photoshop, GIS, etc., to process images; and the other is to write command processing images based on Matlab, visual C++ and other mathematical function software. SEM images are widely used in the fields of alloy, rock-soil particles, insulator hydrophobicity and the like through image processing, but analysis and research on microstructure of the SEM images of the insulating paper board by utilizing an image processing technology are still blank.
Disclosure of Invention
The invention aims to provide an insulation paperboard aging judging and checking method based on SEM image processing, which utilizes an image processing technology to analyze the microstructure of an SEM image of the insulation paperboard, judges the aging degree of the insulation paperboard by combining with the test criteria of fiber diameter, section porosity and roughness and three-dimensional model judgment, and provides data support for prolonging the service life of the insulation paperboard and manufacturing novel insulation paperboard.
In order to achieve the above object, the present invention provides the following solutions:
an insulation board aging discrimination inspection method based on SEM image processing comprises the following steps:
s1, preparing a plurality of insulating paperboard samples, and acquiring a front SEM image and a cross-section SEM image of the insulating paperboard samples by using a scanning electron microscope;
s2, performing image preprocessing on the front SEM image and the cross-section SEM image by using image enhancement, image denoising and segmentation methods;
s3, respectively extracting fiber edge images in the front SEM image and the cross-section SEM image after pretreatment;
s4, calculating the fiber diameter and the section porosity based on the fiber edge image, drawing a three-dimensional graph of the fiber microscopic morphology by using matlab, and calculating the roughness;
s5, repeating the step S1, obtaining a plurality of continuous cross-section SEM images, and reconstructing a three-dimensional model of the fiber by utilizing fiber edge images extracted from the continuous cross-section SEM images;
s6, analyzing the microscopic morphological defects of the fibers, and judging the aging degree of the insulating paperboard by combining the test criteria of the fiber diameter, the section porosity and the roughness and the three-dimensional model judgment.
Optionally, in step S1, a plurality of insulating paper board samples are prepared, and a front SEM image and a cross-section SEM image of the insulating paper board samples are obtained by using a scanning electron microscope, which specifically includes:
cutting a plurality of insulating paperboard samples to a certain size, and putting the insulating paperboard samples into a scanning electron microscope to obtain SEM images of the front surfaces of a plurality of insulating paperboards;
and (3) polishing the insulating paperboard sample along the section direction by using 5000-mesh abrasive paper, measuring the thickness by using a micrometer after each polishing, controlling the polishing thickness to be about 10 mu m+/-2 mu m, and putting the insulating paperboard sample into a scanning electron microscope to obtain an SEM image of the cross section of the insulating paperboard.
Optionally, in the step S2, image preprocessing is performed on the front SEM image and the cross-section SEM image by using image enhancement, image denoising and segmentation methods, and specifically includes:
the method for combining the image enhancement selection piecewise linear enhancement and the histogram equalization algorithm improves the contrast ratio of fiber contours and the background in the image, and obtains an SEM image with high contrast ratio;
the image denoising adopts an adaptive median filtering algorithm.
Optionally, in step S3, the fiber edge images in the front SEM image and the cross-section SEM image after the pretreatment are respectively extracted, which specifically includes:
adopting a canny algorithm to construct five-step degree templates of 8 directions of 0 degree, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees and 157.5 degrees of central pixels, and respectively calculating the first-order partial derivative finite difference of each direction, the gradient amplitude and the gradient direction;
after the partial derivative matrix is obtained, non-maximum suppression is carried out on the gradient amplitude, the peak-valley value of the gray level histogram is selected to be set as the initial high threshold value and the low threshold value, the gradient amplitude is binarized, edge extraction is carried out on the gradient amplitude, the double threshold value is adjusted according to the extraction result, and finally the fiber edge image is obtained.
Optionally, in the step S4, based on the fiber edge image, the fiber diameter and the section porosity are calculated, and a fiber micro-morphology three-dimensional map is drawn by using matlab, and the roughness is calculated, which specifically includes:
calculating the fiber diameter: measuring the diameter of theta fibers in the extracted edge image by using a matlab kit, and taking a drawn diameter frequency distribution map and a normal distribution fitting curve as an aging judgment basis; at the same time, the peak value of the fitting curve corresponds to the diameter l 1 Centering on, l k For expanding the radius, let the diameter be l 1 ±l k The fiber number in the range is 75% of the total detected fiber number, and the diameter is defined as l 1 ±l k Fibers in the range are defined as the cardboard representing the fiber groups to represent the average diameter of the fiber groups
As a board ageing characteristic value;
calculating the section porosity: calculating the number of pixels surrounded by the hole edges between fiber contours in the extracted sectional images by taking one pixel point as a minimum area unit, namely marking the total hole area of the sections as S, and comparing the total hole area with the area S of the total area of the images to obtain the porosity
The porosity calculation is carried out on all the acquired sectional images, namely { gamma } 1 ，γ 2 ，γ 3 ……γ τ Mean>
Is the fiber section porosity;
calculating roughness: the gray value of each pixel is used as a bottom, and the gray value of the pixel is used as a high to represent a fiber microscopic appearance three-dimensional map of the image by utilizing the characteristics that the gray value of the pixel is higher when particles are closer to a light source and smaller when particles are farther from the light source in an SEM image;
carrying out normalization processing on the gray level value of the SEM image, and calculating the average gray level value of the normalized gray level image:
wherein M and N are the number of discrete samples in the x-direction and y-direction, respectively, i.e., X, Y value of the image, F (x) i ，y j ) For gray values after normalization to between 0 and 1, the surface roughness of the insulating board is characterized by a three-dimensional arithmetic mean deviation Sa:
recording the three-dimensional arithmetic mean deviation of SEM images of the front side of the sigma-sheet insulating paperboard as S respectively a1 ，S 2 ，…S ai …S aσ Setting:
the obtained
Is the average roughness.
Optionally, in the step S5, the reconstructing the three-dimensional model of the fiber specifically includes:
the fiber three-dimensional model is reconstructed using the shortest diagonal method.
Optionally, in the step S6, the defect of the fiber microstructure is analyzed, and the aging degree of the insulating paper board is judged by combining with the test criteria of the fiber diameter, the section porosity and the roughness and the three-dimensional model judgment, which specifically includes:
comparing the fiber models before and after aging aiming at the three-dimensional model to observe microscopic changes of the fibers after aging so as to preliminarily judge whether the insulating paperboard is aged or not;
and (3) obtaining the aging value of the insulating paperboard by combining the fiber diameter, the section porosity and the roughness:
wherein,,
the assigned values of fiber diameter, section porosity and roughness are respectively shown, and the aging criterion is as follows:
according to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the SEM image processing-based insulation paperboard aging judging and checking method provided by the invention, SEM images of the insulation paperboard under different aging conditions are obtained through a scanning electron microscope; the fiber outline and the section holes of the insulating paperboard are extracted by adopting an image processing technology, so that the fiber diameter and the section porosity can be calculated; calculating roughness and drawing a fiber microscopic three-dimensional morphology graph by utilizing the characteristic that the gray value of a pixel in an SEM image is higher when particles are closer to a light source and smaller when the particles are farther from the light source; the change trend of the micro characteristic quantity of the paper board before and after aging is obtained from the obtained specific microscopic information and the fiber three-dimensional model, so that data support is provided for researching the long-term service capability of the paper board in one step and improving the insulating property.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an exemplary SEM image processing based insulation board aging discrimination inspection method;
FIG. 2 is a schematic view of surface topography sampling of an insulation board according to an embodiment of the invention;
FIG. 3 is an SEM original view of an insulating paperboard in an embodiment of the invention;
FIG. 4 is a schematic drawing of an insulation board fiber extraction in an embodiment of the invention;
FIG. 5 is a schematic drawing showing the extraction of voids from the cross-section of an insulating paperboard in an embodiment of the invention;
FIG. 6 is a schematic view of the fiber diameter distribution of an insulation board according to an embodiment of the present invention;
FIG. 7 is a three-dimensional schematic view of the microtopography of an insulating paperboard in an embodiment of the invention;
fig. 8 is a three-dimensional reconstruction model of an insulation board fiber in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide an insulation paperboard aging judging and checking method based on SEM image processing, which utilizes an image processing technology to analyze the microstructure of an SEM image of the insulation paperboard, judges the aging degree of the insulation paperboard by combining with the test criteria of fiber diameter, section porosity and roughness and three-dimensional model judgment, and provides data support for prolonging the service life of the insulation paperboard and manufacturing novel insulation paperboard.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the method for inspecting the aging discrimination of the insulating paperboard based on SEM image processing provided by the embodiment of the invention comprises the following steps:
s1, preparing a plurality of insulating paperboard samples, and acquiring a front SEM image and a cross-section SEM image of the insulating paperboard samples by using a scanning electron microscope;
s2, performing image preprocessing on the front SEM image and the cross-section SEM image by using image enhancement, image denoising and segmentation methods;
s3, respectively extracting fiber edge images in the front SEM image and the cross-section SEM image after pretreatment;
s4, calculating the fiber diameter and the section porosity based on the fiber edge image, drawing a three-dimensional graph of the fiber microscopic morphology by using matlab, and calculating the roughness;
s5, repeating the step S1, obtaining tau continuous cross-section SEM images, and reconstructing a three-dimensional model of the fiber by utilizing fiber edge images extracted from the continuous cross-section SEM images;
s6, analyzing the microscopic morphological defects of the fibers, and judging the aging degree of the insulating paperboard by combining the test criteria of the fiber diameter, the section porosity and the roughness and the three-dimensional model judgment.
The step S1 is to prepare a plurality of insulating paperboard samples, and acquire a front SEM image and a cross-section SEM image of the insulating paperboard samples by using a scanning electron microscope, and specifically comprises the following steps:
cutting a plurality of insulating paperboard samples to a certain size, and putting the insulating paperboard samples into a scanning electron microscope to obtain SEM images of the front surfaces of a plurality of insulating paperboards; specifically, as shown in fig. 2, five parts of the cardboard in the upper, lower, left, right and middle are sampled along the diagonal line, cut into square blocks with the size of 1 multiplied by 1cm, put into a vacuum drying oven for drying treatment, because the insulated cardboard after drying treatment is non-conductive, before SEM image analysis, a small ion sputtering instrument with the model of SBC-12 is used for coating, after coating, cardboard samples with different ageing states are put into a scanning electron microscope, the scanning electron microscope model of EM-30Plus is used for observing the microscopic morphology of the cardboard surface, and sigma-piece front SEM pictures are obtained, as shown in fig. 3, the image is fixed compared with the magnification of a real object;
and (3) polishing the insulating paperboard sample along the section direction by using 5000-mesh abrasive paper, measuring the thickness by using a micrometer after each polishing, controlling the polishing thickness to be about 10 mu m+/-2 mu m, and putting the insulating paperboard sample into a scanning electron microscope to obtain an SEM image of the cross section of the insulating paperboard.
In the step S2, image preprocessing is performed on the front SEM image and the cross-section SEM image by using image enhancement, image denoising and segmentation methods, and specifically includes:
the method for combining the image enhancement selection piecewise linear enhancement and the histogram equalization algorithm improves the contrast ratio of fiber contours and the background in the image, and obtains an SEM image with high contrast ratio;
the image denoising adopts an adaptive median filtering algorithm.
The SEM image has the following features: sampling from five positions of the upper part, the lower part, the left part and the right part of the paperboard, carrying out SEM (scanning electron microscope) detection to obtain a plurality of clear SEM images, wherein the images are fixed in magnification compared with a real object, the images are represented by T, and the pixels of all the images are the same and are M multiplied by N;
the image denoising has the following characteristics, and the purpose is to strengthen the image, the improved self-adaptive median filtering algorithm is applied, the recognition of noise points is more sensitive, the defects of insufficient noise reduction capability and overlarge filtering window caused by overlarge filtering window are overcome, and the image distortion is caused, so that the image quality is effectively improved. The specific method comprises the following steps:
setting a 3×3 window Y, placing it in the image T, traversing and scanning, setting 9 numbers in window as T i-1j-1 ，T i-1j ，T i-1j+1 ，T ij-1 ，T ij ，T ij+1 ，T i+1j-1 ，T i+1j ，T i+1j+1 And the size of the materials is arranged, and the maximum value Y is set max Minimum value is Y min Average value is Y ave Let median value Y m The method comprises the following steps:
Y m ＝Me d{T ab |a，b＝i-1，i，i+1} (1)
calculate T ab Deviation value from its surrounding points:
Y p ＝|T ab -T ij |，a，b＝i-1，i，i+1 (2)
the maximum value of the deviation value is marked as Y pm ＝max{Y p Counting Y of all pixels pm Setting two detection thresholdsY p1 ，Y p2 So that 90% of the pixel points Y pm The following conditions are satisfied:
Y p1 ＜Y pm ＜Y p1 (3)
according to the set threshold value, for T ij The specific judgment rules are as follows:
counting the number k of noise in the window Y, and calculating the noise density D:
if D is less than 25%, filtering is performed under n×n window to let T ij ＝Y ave ；
If D is more than 25% and less than 50%, filtering operation is performed under n×n window to make T ij ＝Y m ；
If D is more than or equal to 50%, enlarging the size of a filtering window, and recalculating the noise density D;
when n=3, 50% of D, let T ij ＝T ij 。
For T ij Repeating the above operation for all pixel points in the image T after filtering Wave 。
The image enhancement aims to improve the contrast between fiber contours and the background in an image, obtain an SEM image with high contrast, and select a method combining linearity and a histogram equalization algorithm, which comprises the following steps:
1) For T Wave Obtaining T using linear transformation z1 Drawing T Wave Gray histogram, let T Wave The gray value of (i, j) is T h The gray scale range is [ h ] 1 ，h 2 ]Determining the range of the extracted fiber pixels as [ beta ] according to the wave crest 1 ，β 2 ]Improving contrast, i.e. stretching grey scale range [ beta ] 1 ，β 2 ]To [ beta ]' 1 ，β′ 2 ]Stretching [ h ] 1 ，h 2 ]To [ h ]' 1 ，h′ 2 ]For T h The linear piecewise transform expression employed is as follows:
for T Wave After the gray value of all the pixel points in the image T is converted, the image T is obtained z1 。
2) For T Wave T is obtained by using a histogram equalization method z2 Can enlarge T Wave The gray distribution range, the corrected image brightness, the mapping transformation formula used is as follows:
wherein the method comprises the steps of
Is T Wave Minimum gray value of middle pixel, +.>
Is the maximum gray value, T h2 The gray value of the image is obtained after histogram equalization. For T Wave The gray values of all the pixel points in the image T are obtained after the above conversion z2 。
3) And (3) combining the linear transformation and the histogram equalization algorithm by using the formula (8) to finally obtain an image T ', and achieving the purpose of improving the contrast ratio of the T' by continuously adjusting the coefficient delta.
T′＝δT z1 +(1-δ)T i2 (8)
Wherein T is z1 Is T Wave Results after linear enhancement, T z2 For the results after using the histogram equalization algorithm, δ is a scaling factor, and 0.2 is chosen according to the experiment.
In the step S3, fiber edge images in the front SEM image and the cross-section SEM image after pretreatment are respectively extracted, which specifically includes:
adopting a canny algorithm to construct five-step degree templates of 8 directions of 0 degree, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees and 157.5 degrees of central pixels, and respectively calculating the first-order partial derivative finite difference of each direction, the gradient amplitude and the gradient direction;
the fiber contour and hole edge extraction method has the following characteristics that an improved canny algorithm is adopted to construct a five-step template with 8 directions, such as a formula (9).
Compared with the four-direction Sobel operator, the template is added with the detection direction, and the first-order partial derivative finite difference, gradient amplitude and gradient direction of the central pixel 0 degree, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees and 157.5 degrees can be calculated respectively.
Taking the 0 ° direction as an example, the partial derivative array is:
the gradient amplitude is:
the gradient direction is:
non-maximum suppression is carried out on the gradient amplitude, a gray level histogram is drawn, and the peak value gamma of the histogram is obtained 1 ，γ 2 Setting to be an initial high and low double threshold, binarizing the gradient amplitude, extracting the edge of the image T ', and continuously adjusting the double threshold according to the extraction effect until a complete and clear fiber edge image T' is obtained, as shown in fig. 4.
The 8 azimuth gradient operators are constructed as above, the closer the distance from the neighborhood pixel point to the center pixel is, the larger the weight is if the angle is smaller, and the opposite is if the angle is smaller.
In the step S4, based on the fiber edge image, the fiber diameter and the section porosity are calculated, and a fiber micro-morphology three-dimensional map is drawn by using matlab, and the roughness is calculated, which specifically includes:
the fiber diameter is the average value of the thickness of the paper board representative fiber group, which is one of the aging characteristic values, and as shown in fig. 5, the fiber diameter is calculated: measuring the diameter of theta fibers in the extracted edge image by using a matlab kit, and taking a drawn diameter frequency distribution map and a normal distribution fitting curve as an aging judgment basis; at the same time, the peak value of the fitting curve corresponds to the diameter l 1 Centered at 41 μm, l in FIG. 5 k For the radius expansion range, 5 μm in FIG. 5, let the diameter be l 1 ±l k The fiber number in the range is 75% of the total detected fiber number, and the diameter is defined as l 1 ±l k Fibers in the range are defined as the cardboard representing the fiber groups to represent the average diameter of the fiber groups
As a board ageing characteristic value;
the fiber hole area is one of the aging characteristic values, and as shown in fig. 6, the section porosity is calculated: calculating the number of pixels surrounded by the hole edges between fiber contours in the extracted sectional images by taking one pixel point as a minimum area unit, namely marking the total hole area of the sections as S, and comparing the total hole area with the area S of the total area of the images to obtain the porosity
The porosity calculation is carried out on all the acquired sectional images, namely { gamma } 1 ，γ 2 ，γ 3 .....γ τ Mean>
Is the fiber section porosity;
as shown in fig. 7, roughness was calculated: the gray value of each pixel is used as a bottom, and the gray value of the pixel is used as a high to represent a fiber microscopic appearance three-dimensional map of the image by utilizing the characteristics that the gray value of the pixel is higher when particles are closer to a light source and smaller when particles are farther from the light source in an SEM image;
the gray scale value of the SEM image ranges from 0 to 255, and the normalized gray scale value ranges from normalized to 0 to 1 for convenient observation and comparison. Carrying out normalization processing on the gray level value of the SEM image, and calculating the average gray level value of the normalized gray level image:
wherein M and N are the number of discrete samples in the x-direction and y-direction, respectively, i.e., X, Y value of the image, F (x) i ，y j ) For gray values after normalization to between 0 and 1, the surface roughness of the insulating board is characterized by a three-dimensional arithmetic mean deviation Sa:
recording the three-dimensional arithmetic mean deviation of SEM images of the front side of the sigma-sheet insulating paperboard as S respectively a1 ，S 2 ，…S ai …S aσ Setting:
the obtained
Is one of the average roughness, i.e. the characteristic values of ageing of the board.
In the step S5, the reconstructing the three-dimensional model of the fiber specifically includes:
the fiber three-dimensional model is reconstructed using the shortest diagonal method.
The three-dimensional fiber reconstruction is characterized in that a triangular mesh model is built by utilizing continuous cross section slice images and using a shortest diagonal method, a three-dimensional fiber model is reconstructed, and fiber models before and after aging are compared to observe microscopic changes of the aged fibers.
And reconstructing a three-dimensional model by using the extracted cross-sectional fiber profile. Because of the problems of sample placement, focusing and the like, the central positions of two adjacent cross section fiber contour lines may be greatly different, so that the central points of the adjacent contour lines are necessarily coincident and the size proportion is consistent.
Wherein PanFactor is the translation distance of the Contour line, zoomFactor is the scaling factor, contourr 1 And Contours 2 Representing two adjacent contour lines, respectively.
The triangle mesh model is constructed by using the shortest diagonal method. Two ends of a contour line segment are connected with one point on the contour line on the adjacent cross section, namely, a triangular surface is formed, and three-dimensional reconstruction is realized by utilizing a series of contour lines, namely, the contour lines on two surfaces are connected by utilizing a series of triangular surface pieces. Since the contour lines have been pre-processed in advance, the shortest diagonal method can be selected for use. The shortest distance between two points of two adjacent contour lines can be selected by the following steps:
wherein Q is x 、Q y For the coordinate value of the current pixel point, P x 、P y The coordinate values closest to the current point, respectively.
By adopting the method, all the contour lines are connected to form a triangular mesh model, the triangular mesh model is rendered to complete reconstruction, and as shown in fig. 8, whether the insulating paperboard has ageing possibility can be primarily judged by a direct observation method.
In the step S6, the defect of the fiber microstructure is analyzed, and the aging degree of the insulating paper board is judged by combining with the test criteria of the fiber diameter, the section porosity and the roughness and the three-dimensional model judgment, which specifically comprises the following steps:
comparing the fiber models before and after aging aiming at the three-dimensional model to observe microscopic changes of the fibers after aging so as to preliminarily judge whether the insulating paperboard is aged or not;
and (3) obtaining the aging value of the insulating paperboard by combining the fiber diameter, the section porosity and the roughness:
wherein,,
the assigned values of fiber diameter, section porosity and roughness are respectively shown, and the aging criterion is as follows:
and (3) carrying out ageing characteristic value calculation on the unaged paper board and the ageing paper board, and normalizing the ageing parameters by taking the unaged paper board parameters as the standard.
According to research analysis, the fiber diameter of the paper board after aging tends to be reduced, the porosity of the cross section is increased, and the surface roughness is slightly reduced in the initial stage of aging due to ablation, but is gradually increased quickly. Normalization was performed on the unaged cardboard, and when the cardboard fiber diameter was below 0.9908, the porosity was 1.0785, and the roughness was below 1, the cardboard was judged to begin to age. However, because of errors in the process of detecting the parameters of the sample, a single characteristic quantity cannot be used as an aging test standard, and the relationship between the fiber diameter, the porosity, the roughness and the aging of the paper board needs to be comprehensively considered. The specific operation is as follows: 1. assigning a score to each characteristic value as shown in table 1; 2. because of the porosity, the roughness is to detect the whole paperboard, the diameter is the measurement result of the fiber to the individual, and certain contingency exists, and according to the proportional coefficient of 0.4,0.4,0.2, all characteristic values are comprehensively considered to judge whether the paperboard is aged or not.
Table 1 scoring criteria for each eigenvalue
Note that: the data in the tables are based on the parameters of the unaged paperboard, and the fiber diameter, porosity and roughness of the unaged paperboard are all 1.
Judging the ageing degree of the paperboard: measuring the characteristic values of the sample to be detected and the unaged paper board respectively; normalizing each characteristic value by taking an unaged paper board as a reference; assigning a score to each characteristic value according to table 1; substituting the characteristic value after the assignment into a formula (19) to calculate Age according to the proportionality coefficient of 0.4,0.4,0.2,
to be used for
For example, the table is checked up,
substituting formula (19), calculated ++>
The board can be judged to be aged.
According to the SEM image processing-based insulation paperboard aging judging and checking method provided by the invention, SEM images of the insulation paperboard under different aging conditions are obtained through a scanning electron microscope; the fiber outline and the section holes of the insulating paperboard are extracted by adopting an image processing technology, so that the fiber diameter and the section porosity can be calculated; calculating roughness and drawing a fiber microscopic three-dimensional morphology graph by utilizing the characteristic that the gray value of a pixel in an SEM image is higher when particles are closer to a light source and smaller when the particles are farther from the light source; the change trend of the micro characteristic quantity of the paper board before and after aging is obtained from the obtained specific microscopic information and the fiber three-dimensional model, so that data support is provided for researching the long-term service capability of the paper board in one step and improving the insulating property.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (6)
1. An insulation paperboard aging discrimination inspection method based on SEM image processing is characterized by comprising the following steps:
s1, preparing a plurality of insulating paperboard samples, and acquiring a front SEM image and a cross-section SEM image of the insulating paperboard samples by using a scanning electron microscope;
s2, performing image preprocessing on the front SEM image and the cross-section SEM image by using image enhancement, image denoising and segmentation methods;
s3, respectively extracting fiber edge images in the front SEM image and the cross-section SEM image after pretreatment;
s4, calculating the fiber diameter and the section porosity based on the fiber edge image, drawing a three-dimensional graph of the fiber microscopic morphology by using matlab, and calculating the roughness;
s5, repeating the step S1, obtaining a plurality of continuous cross-section SEM images, and reconstructing a three-dimensional model of the fiber by utilizing fiber edge images extracted from the continuous cross-section SEM images;
s6, analyzing microscopic morphological defects of the fiber, and judging the aging degree of the insulating paperboard by combining test criteria of fiber diameter, section porosity and roughness and three-dimensional model judgment;
s7, comparing the fiber models before and after aging aiming at the three-dimensional model to observe microscopic changes of the fibers after aging so as to preliminarily judge whether the insulating paperboard is aged or not;
and (3) obtaining the aging value of the insulating paperboard by combining the fiber diameter, the section porosity and the roughness:
wherein,,
the assigned values of fiber diameter, section porosity and roughness are respectively shown, and the aging criterion is as follows:
2. the method for inspecting the aging discrimination of the insulating paper board based on the SEM image processing according to claim 1, wherein in the step S1, a plurality of insulating paper board samples are prepared, and a front SEM image and a cross-section SEM image of the insulating paper board samples are obtained by using a scanning electron microscope, specifically comprising:
cutting a plurality of insulating paperboard samples to a certain size, and putting the insulating paperboard samples into a scanning electron microscope to obtain SEM images of the front surfaces of a plurality of insulating paperboards;
and (3) polishing the insulating paperboard sample along the section direction by using 5000-mesh abrasive paper, measuring the thickness by using a micrometer after each polishing, controlling the polishing thickness to be 10 mu m+/-2 mu m, and putting the insulating paperboard sample into a scanning electron microscope to obtain an SEM image of the cross section of the insulating paperboard.
3. The method for inspecting the aging discrimination of the insulating paper board based on the SEM image processing according to claim 1, wherein in the step S2, the front SEM image and the cross-sectional SEM image are subjected to image preprocessing by using image enhancement, image denoising and segmentation methods, specifically comprising:
the method for combining the image enhancement selection piecewise linear enhancement and the histogram equalization algorithm improves the contrast ratio of fiber contours and the background in the image, and obtains an SEM image with high contrast ratio;
the image denoising adopts an adaptive median filtering algorithm.
4. The method according to claim 1, wherein in step S3, fiber edge images in the pre-processed front SEM image and the cross-sectional SEM image are extracted respectively, and the method specifically comprises:
adopting a canny algorithm to construct five-step degree templates of 8 directions of 0 degree, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees and 157.5 degrees of central pixels, and respectively calculating the first-order partial derivative finite difference of each direction, the gradient amplitude and the gradient direction;
after the partial derivative matrix is obtained, non-maximum suppression is carried out on the gradient amplitude, the peak-valley value of the gray level histogram is selected to be set as the initial high threshold value and the low threshold value, the gradient amplitude is binarized, edge extraction is carried out on the gradient amplitude, the double threshold value is adjusted according to the extraction result, and finally the fiber edge image is obtained.
5. The method according to claim 1, wherein in step S4, fiber diameter and section porosity are calculated based on fiber edge image, and fiber microscopic morphology three-dimensional map is drawn by matlab, and roughness is calculated, specifically comprising:
calculating the fiber diameter: measuring the diameter of theta fibers in the extracted edge image by using a matlab kit, and taking a drawn diameter frequency distribution map and a normal distribution fitting curve as an aging judgment basis; at the same time, the peak value of the fitting curve corresponds to the diameter l 1 Centering on, l k For expanding the radius, let the diameter be l 1 ±l k The fiber number in the range is 75% of the total detected fiber number, and the diameter is defined as l 1 ±l k Fibers in the range are defined as the cardboard representing the fiber groups to represent the average diameter of the fiber groups
As a board ageing characteristic value;
calculating the section porosity: to be used forOne pixel point is used as a minimum area unit, the number of pixels surrounded by the hole edges between fiber contours in the extracted sectional image is calculated, namely the total hole area of the section is recorded as S, and the total hole area is compared with the area S of the total area of the image to obtain the porosity
The porosity calculation is carried out on all the acquired sectional images, namely { gamma } 1 ，γ 2 ，γ 3 ……γ τ Mean>
Is the fiber section porosity;
calculating roughness: the gray value of each pixel is used as a bottom, and the gray value of the pixel is used as a high to represent a fiber microscopic appearance three-dimensional map of the image by utilizing the characteristics that the gray value of the pixel is higher when particles are closer to a light source and smaller when particles are farther from the light source in an SEM image;
carrying out normalization processing on the gray level value of the SEM image, and calculating the average gray level value of the normalized gray level image:
wherein M and N are the number of discrete samples in the x-direction and y-direction, respectively, i.e., X, Y value of the image, T (x) i ,y j ) For the gray value after normalization to 0-1, the surface roughness of the insulating paper board is calculated by three-dimensional arithmetic mean deviation S a Characterization:
recording the three-dimensional arithmetic mean deviation of SEM images of the front side of the sigma-sheet insulating paperboard as S respectively a1 ,S 2 ,…S ai …S aσ Setting:
the obtained
Is the average roughness.
6. The method for inspecting the aging discrimination of the insulating paper board based on the SEM image processing according to claim 1, wherein in the step S5, the three-dimensional model reconstruction is performed on the fiber, specifically including: the fiber three-dimensional model is reconstructed using the shortest diagonal method.
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