CN112116583A - Insulated paperboard aging discrimination and inspection method based on SEM image processing - Google Patents
Insulated paperboard aging discrimination and inspection method based on SEM image processing Download PDFInfo
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Abstract
The invention discloses an insulated paperboard aging distinguishing and testing method based on SEM image processing, which comprises the following steps: acquiring a front SEM image and a cross-section SEM image of an 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 the diameter, the section porosity and the roughness of the fiber based on the fiber edge image; performing three-dimensional model reconstruction on the fiber by using fiber edge images extracted from continuous cross-section SEM images; analyzing the microscopic morphology defects of the fibers, and judging the aging degree of the insulating paperboard by combining the fiber diameter, section porosity and roughness test criteria and three-dimensional model judgment. According to the insulated paperboard aging judging and inspecting method based on SEM image processing, provided by the invention, the microstructure of the SEM image of the insulated paperboard is analyzed by using an image processing technology, so that data support is provided for prolonging the service life of the insulated paperboard and manufacturing a novel insulated paperboard.
Description
Technical Field
The invention relates to the technical field of insulation board aging detection, in particular to an insulation board aging distinguishing and detecting method based on SEM image processing.
Background
The insulating paper board is a solid organic insulating material widely used in power transformers, and plays a role in determining the insulating performance of the transformers. However, in the long-term operation of the transformer, the insulating paper board will gradually deteriorate under the action of various complex operation environments, and the insulating performance is gradually reduced. Research shows that the main component of the insulating paper board, namely cellulose, can generate depolymerization reactions and elimination reactions under the action of a complex environment, the depolymerization reactions can generate a large amount of derivatives, and vicious cycles can cause other more complex chemical reactions, and the reactions can change the fiber structure continuously, so that the insulating paper is aged. Through carrying out aging test to insulating board, inspect cardboard microcosmic characteristic under the different ageing degree, to studying insulating board ageing law, the analysis ageing mechanism to improve insulating board life, further make novel insulating board and have important meaning.
In the aspect of judging the aging state of the oil-impregnated insulating paper, a method for determining the aging rate of the oil-impregnated insulating paper by measuring the Tensile Strength (TS) of the insulating paper is mostly adopted at first, generally, when the TS is reduced to half of an initial value, the insulating paper enters the late service life, and secondly, the polymerization degree is also one of aging parameter indexes. In recent years, with the improvement of the precision and the magnification of observation equipment, equipment such as FT-IR, UV-Vis, AFM, SEM and the like is applied to the field of the research on the aging state of insulating oil paper. The Scanning Electron Microscope (SEM) can visually analyze the quality of the paperboard and the fiber damage process by observing the microscopic morphology of the surface of the paperboard, is widely applied to the research of the aging mechanism of the paperboard, but can only obtain a direct observation result by observing an SEM image, cannot obtain further deep statistical information, and needs more clear data in order to accurately judge the aging degree of the insulating paperboard.
Currently, there are two main analytical approaches to processing SEM images: one is based on mature image processing software, such as Photoshop, GIS, etc., to process images; the other method is to write a command to process the image based on mathematical function software such as Matlab, Visual C + +, and the like. The analysis of SEM images by image processing is widely applied to the fields of alloy, rock and soil particles, insulator hydrophobicity and the like, but the analysis and research of the microstructure of the SEM images of the insulating paper boards by using the image processing technology are still blank.
Disclosure of Invention
The invention aims to provide an insulation paperboard aging distinguishing and testing method based on SEM image processing, which analyzes the microstructure of an SEM image of an insulation paperboard by utilizing an image processing technology, distinguishes the aging degree of the insulation paperboard by combining the detection 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 a novel insulation paperboard.
In order to achieve the purpose, the invention provides the following scheme:
an insulation paperboard aging distinguishing and inspecting method based on SEM image processing comprises the following steps:
s1, preparing a plurality of insulating paperboard samples, and acquiring front SEM images and cross-section SEM images 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 preprocessed front SEM image and cross-section SEM image;
s4, calculating the fiber diameter and the section porosity based on the fiber edge image, drawing a fiber micro-morphology three-dimensional graph by using matlab, and calculating the roughness;
s5, repeating the step S1, obtaining a plurality of continuous cross-section SEM images, and performing three-dimensional model reconstruction on the fibers by using fiber edge images extracted from the continuous cross-section SEM images;
and S6, analyzing the micro-morphology defects of the fibers, and judging the aging degree of the insulating paperboard by combining the detection criteria of fiber diameter, section porosity and roughness and the judgment of a three-dimensional model.
Optionally, in step S1, preparing a plurality of insulating paperboard samples, and acquiring a front SEM image and a cross-sectional SEM image of the insulating paperboard sample by using a scanning electron microscope, specifically including:
cutting a plurality of insulating paper board samples to a certain size, and putting the insulating paper board samples into a scanning electron microscope to obtain SEM images of the front surfaces of a plurality of insulating paper boards;
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 polishing each time, controlling the polishing thickness to be about 10 microns +/-2 microns, and putting into a scanning electron microscope to obtain the SEM image of the cross section of the insulating paperboard.
Optionally, in step S2, image preprocessing is performed on the front SEM image and the cross-sectional SEM image by using image enhancement, image denoising, and segmentation methods, which specifically includes:
the image enhancement selects a method combining piecewise linear enhancement and a histogram equalization algorithm, so that the contrast between the fiber outline and the background in the image is improved, and a high-contrast SEM image is obtained;
the image denoising adopts an adaptive median filtering algorithm.
Optionally, in step S3, the extracting fiber edge images in the preprocessed front-side SEM image and cross-section SEM image respectively includes:
adopting a canny algorithm to construct five-step degree templates of 8 directions of central pixels of 0 degrees, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees and 157.5 degrees, and respectively calculating first-order partial derivative finite differences, gradient amplitudes and gradient directions of all the directions;
after a partial derivative matrix is solved, non-maximum suppression is carried out on the gradient amplitude, the peak-valley value of a gray level histogram is selected to be set as the initial height, the gradient amplitude is binarized by a low dual threshold value, edge extraction is carried out on the gradient amplitude, the dual threshold values are adjusted according to the extraction result, and finally a fiber edge image is obtained.
Optionally, in step S4, calculating a fiber diameter and a cross-sectional porosity based on the fiber edge image, drawing a three-dimensional image of a fiber micro-topography by using matlab, and calculating a roughness, specifically including:
calculating the fiber diameter: measuring the diameters of theta fibers in the extracted edge images by using a matlab tool kit, and using the drawn diameter frequency distribution diagram and a normal distribution fitting curve as aging judgment bases; meanwhile, the peak value of the fitted curve corresponds to the diameter l1As a center,. lkThe diameter is increased to be in the range of1±lkThe number of fibers in the range is 75% of the total number of fibers detected, and the diameter is l1±lkFibers within the range are defined as the average diameter of the group of fibers represented by the paperboard
As a board aging characteristic value;
calculating the section porosity: taking a pixel point as a minimum area unit, calculating the number of pixels surrounded by the hole edges between the fiber outlines in the extracted cross-section image, namely the total hole area of the cross-section is recorded as S, and comparing the total hole area with the area S of the total area of the image to obtain the porosity
Calculating the porosity of all the obtained sectional images, namely the calculated porosity is gamma1，γ2，γ3……γτGet the average value
Is the porosity of the cross section of the fiber;
calculating the roughness: using the characteristic that the closer the particles to the light source in the SEM image, the higher the pixel gray value, the farther the particles are from the light source, and the smaller the gray value, and using the gray value of each pixel as the height to represent the fiber micro-morphology three-dimensional image of the image;
normalizing the gray value of the SEM image, and calculating the average gray value of the normalized gray image:
where M and N are the number of discrete sampling points in the x-direction and y-direction, respectively, i.e., the X, Y value of the image, F (x)i，yj) The surface roughness of the insulating paperboard is characterized by the three-dimensional arithmetic mean deviation Sa for the grey values normalized to between 0 and 1:
respectively recording three-dimensional arithmetic mean deviation of SEM images of the front surfaces of the sheets of the insulation paper boards as Sa1，S2，…Sai…SaσSetting:
obtained by
The average roughness is.
Optionally, in step S5, reconstructing a three-dimensional model of the fiber specifically includes:
and reconstructing a fiber three-dimensional model by using a shortest diagonal method.
Optionally, in step S6, analyzing the microscopic morphology defects of the fibers, and determining the aging degree of the insulating paperboard by combining the inspection criteria of fiber diameter, section porosity and roughness and the three-dimensional model judgment, specifically including:
comparing the fiber models before and after aging aiming at the three-dimensional model to observe the microscopic change of the aged fibers 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,
respectively representing the assigned values of fiber diameter, section porosity and roughness, and the aging judgment standard is as follows:
according to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the insulated paperboard aging distinguishing and inspecting method based on SEM image processing, SEM images of insulated paperboards under different aging conditions are obtained through a scanning electron microscope; the fiber profile and the section holes of the insulating paperboard are extracted by adopting an image processing technology, and the fiber diameter and the section porosity can be calculated; calculating roughness and drawing a fiber microscopic three-dimensional topography graph by utilizing the characteristics that the closer particles to a light source in an SEM image, the higher the pixel gray value is, the farther particles are from the light source, and the smaller the gray value is; the change trend of the microscopic characteristic quantity of the paperboard 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 one-step study on the long-term service capability of the paperboard and improvement on the insulating property.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of an insulation board aging discrimination inspection method based on SEM image processing in an example of the present invention;
FIG. 2 is a schematic view of a sample of the surface topography of an insulating paperboard in an embodiment of the invention;
FIG. 3 is an SEM original drawing of an insulating paperboard in an embodiment of the invention;
FIG. 4 is a schematic view of the extraction of the fibers of the insulating paperboard in an embodiment of the invention;
FIG. 5 is a schematic view showing the extraction of the pores in the section of the insulating paperboard in the embodiment of the present invention;
FIG. 6 is a schematic view of the fiber diameter distribution of the insulating paperboard in an embodiment of the invention;
FIG. 7 is a three-dimensional schematic view of the microstructure of the insulating paperboard in an embodiment of the invention;
FIG. 8 is a three-dimensional reconstruction model of the insulating paperboard fibers in the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an insulation paperboard aging distinguishing and testing method based on SEM image processing, which analyzes the microstructure of an SEM image of an insulation paperboard by utilizing an image processing technology, distinguishes the aging degree of the insulation paperboard by combining the detection 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 a novel insulation paperboard.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the method for identifying and inspecting the aging of the insulating paperboard based on SEM image processing according to the embodiment of the present invention includes:
s1, preparing a plurality of insulating paperboard samples, and acquiring front SEM images and cross-section SEM images 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 preprocessed front SEM image and cross-section SEM image;
s4, calculating the fiber diameter and the section porosity based on the fiber edge image, drawing a fiber micro-morphology three-dimensional graph by using matlab, and calculating the roughness;
s5, repeating the step S1, obtaining tau continuous cross-section SEM images, and performing three-dimensional model reconstruction on the fibers by using fiber edge images extracted from the continuous cross-section SEM images;
and S6, analyzing the micro-morphology defects of the fibers, and judging the aging degree of the insulating paperboard by combining the detection criteria of fiber diameter, section porosity and roughness and the judgment of a three-dimensional model.
In step S1, preparing a plurality of insulating paperboard samples, and acquiring a front SEM image and a cross-sectional SEM image of the insulating paperboard sample by using a scanning electron microscope, specifically including:
cutting a plurality of insulating paper board samples to a certain size, and putting the insulating paper board samples into a scanning electron microscope to obtain SEM images of the front surfaces of a plurality of insulating paper boards; specifically, as shown in fig. 2, five parts of the upper part, the lower part, the left part and the right part of the paperboard are sampled along a diagonal line, the paperboard is cut into a square block with the size of 1 × 1cm, the square block is placed into a vacuum drying oven for drying treatment, because the insulating paperboard after drying treatment is not conductive, a small ion sputtering instrument with the model of SBC-12 is used for coating treatment before SEM image analysis, after coating, the paperboard samples with different aging states are placed into a scanning electron microscope with the model of the scanning electron microscope EM-30Plus, the micro-morphology of the surface of the paperboard is observed, and sigma front-side SEM pictures are obtained, and as shown in fig. 3, the pictures are;
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 polishing each time, controlling the polishing thickness to be about 10 microns +/-2 microns, and putting into a scanning electron microscope to obtain the SEM image of the cross section of the insulating paperboard.
In step S2, image preprocessing is performed on the front SEM image and the cross-sectional SEM image by using image enhancement, image denoising, and segmentation methods, which specifically includes:
the image enhancement selects a method combining piecewise linear enhancement and a histogram equalization algorithm, so that the contrast between the fiber outline and the background in the image is improved, and a high-contrast SEM image is obtained;
the image denoising adopts an adaptive median filtering algorithm.
The SEM images had the following characteristics: sampling from five positions of the upper part, the lower part, the left part and the right part of the paper board, carrying out SEM detection, and obtaining a plurality of clear SEM images, wherein the images are fixed compared with the magnification of a real object, are represented by T, and have the same pixels of M multiplied by N;
the image denoising method has the following characteristics, aims to enhance images, applies an improved self-adaptive median filtering algorithm, is more sensitive to the identification of noise points, overcomes the defects of insufficient denoising capability and image distortion caused by overlarge filtering window due to the undersize filtering window, and effectively improves the image quality. The specific method comprises the following steps:
setting a 3X 3 window Y, putting it in the image T, and traversing and scanning, and setting the 9 numbers in the window as Ti-1j-1，Ti-1j，Ti-1j+1，Tij-1，Tij，Tij+1，Ti+1j-1，Ti+1j，Ti+1j+1And arranging them in size, setting the maximum value YmaxMinimum value of YminAverage value of YaveLet the median value YmComprises the following steps:
Ym＝Me d{Tab|a，b＝i-1，i，i+1} (1)
calculating TabDeviation from its surrounding points:
Yp＝|Tab-Tij|，a，b＝i-1，i，i+1 (2)
the maximum value of the deviation value is recorded as Ypm＝max{Yp}, counting Y of all pixel pointspmSetting two detection thresholds Yp1，Yp2So that 90% of the pixels YpmThe following conditions are satisfied:
Yp1＜Ypm＜Yp1 (3)
according to the set threshold value, for TijAnd judging, wherein the specific judgment rule is as follows:
counting the number k of noise points in the window Y, and calculating the noise density D:
if D is less than 25%, performing filtering operation under n × n window to let Tij＝Yave；
If 25% < D < 50%, a filtering operation is performed under a window of n × n, let Tij＝Ym；
If D is larger than or equal to 50%, enlarging the size of a filtering window, and recalculating the noise density D;
when n is 3, 50% of D, let Tij＝Tij。
For TijRepeating the above operations on all the pixel points to obtain a filtered image TWave。
The image enhancement aims at improving the contrast ratio of the fiber outline and the background in the image, obtaining an SEM image with high contrast ratio, and selecting a method for combining a linear and histogram equalization algorithm, and specifically comprises the following steps:
1) for TWaveDeriving T using a linear transformationz1Drawing TWaveHistogram of gray scale, let TWave(i, j) has a gray value of ThThe gray scale range is [ h ]1，h2]Determining the range of the extracted cellulose pixel as [ beta ] according to the wave crest1，β2]Increasing contrast, i.e. stretching the gray scale [ beta ]1，β2]To [ beta'1，β′2]Stretching [ h ]1，h2]To [ h'1，h′2]To ThThe linear piecewise transform expression used is as follows:
for TWaveAfter gray values of all the pixel points are transformed, an image T is obtainedz1。
2) For TWaveObtaining T using histogram equalizationz2Can enlarge TWaveThe gray distribution range and the corrected image brightness use the mapping transformation formula as follows:
wherein
Is TWaveThe minimum gray value of the middle pixel,
is the maximum gray value, Th2The gray value of the image after histogram equalization is carried out. For TWaveAfter the gray values of all the pixel points are converted as above, the image T is obtainedz2。
3) And (3) combining the linear transformation and the histogram equalization algorithm by using a formula (8) to finally obtain an image T ', and achieving the purpose of improving the contrast of T' by continuously adjusting the coefficient.
T′＝Tz1+(1-)Ti2 (8)
Wherein, Tz1Is TWaveAfter linear enhancement, Tz2For the results after the histogram equalization algorithm, the scale factor was chosen to be 0.2 according to the experiment.
In step S3, the extracting fiber edge images in the preprocessed front-side SEM image and cross-section SEM image respectively includes:
adopting a canny algorithm to construct five-step degree templates of 8 directions of central pixels of 0 degrees, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees and 157.5 degrees, and respectively calculating first-order partial derivative finite differences, gradient amplitudes and gradient directions of all the directions;
the fiber contour and hole edge extraction method has the following characteristics that a five-step degree template with 8 orientations is constructed by adopting an improved canny algorithm, and the five-step degree template is shown in a formula (9).
Compared with a four-direction Sobel operator, the template increases the detection direction, and can calculate the first-order partial derivative finite difference, the gradient amplitude and the gradient direction of the central pixel in the directions of 0 degrees, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees and 157.5 degrees respectively.
Taking the 0 ° direction as an example, the partial derivative array is:
the gradient amplitude is:
the gradient direction is as follows:
performing non-maximum suppression on the gradient amplitude, drawing a gray histogram, and performing peak-to-valley value gamma of the histogram1，γ2Setting the gradient amplitude value to be initial high and low dual threshold values, carrying out binarization on the gradient amplitude value, carrying out edge extraction on the image T ', and continuously adjusting the dual threshold values according to the extraction effect until a complete and clear fiber edge image T' is obtained, as shown in FIG. 4.
The constructed 8 orientation gradient operators are as above, the closer the distance from the neighborhood pixel point to the central pixel is, the smaller the angle is, the larger the weight is, and the reverse is true.
In step S4, calculating the fiber diameter and the cross-sectional porosity based on the fiber edge image, drawing a three-dimensional image of the fiber micro-topography by using matlab, and calculating the roughness, specifically including:
the fiber diameter is the average value of the thickness of the representative fiber group of the paperboard, and is one of aging characteristic values, and as shown in fig. 5, the fiber diameter is calculated as follows: measuring the diameters of theta fibers in the extracted edge images by using a matlab tool kit, and using the drawn diameter frequency distribution diagram and a normal distribution fitting curve as aging judgment bases; meanwhile, the peak value of the fitted curve corresponds to the diameter l1Centered at 41 μm, l in FIG. 5kFor radius enlargement, 5 μm in FIG. 5, let the diameter be l1±lkThe number of fibers in the range is 75% theta of the total number of fibers detected, and the diameter is l1±lkFibers within the range are defined as the average diameter of the group of fibers represented by the paperboard
As a board aging characteristic value;
the fiber pore area is one of the aging characteristic values, and as shown in fig. 6, the cross-sectional porosity is calculated: taking a pixel point as a minimum area unit, calculating the number of pixels surrounded by the hole edges between the fiber outlines in the extracted cross-section image, namely the total hole area of the cross-section is recorded as S, and comparing the total hole area with the area S of the total area of the image to obtain the porosity
Calculating the porosity of all the obtained sectional images, namely the calculated porosity is gamma1，γ2，γ3.....γτGet the average value
Is the porosity of the cross section of the fiber;
as shown in fig. 7, the roughness is calculated: using the characteristic that the closer the particles to the light source in the SEM image, the higher the pixel gray value, the farther the particles are from the light source, and the smaller the gray value, and using the gray value of each pixel as the height to represent the fiber micro-morphology three-dimensional image of the image;
the gray scale value range of the SEM image is between 0 and 255, and the normalization gray scale value range is between 0 and 1 for convenient observation and contrast. Normalizing the gray value of the SEM image, and calculating the average gray value of the normalized gray image:
where M and N are the number of discrete sampling points in the x-direction and y-direction, respectively, i.e., the X, Y value of the image, F (x)i，yj) The surface roughness of the insulating paperboard is characterized by the three-dimensional arithmetic mean deviation Sa for the grey values normalized to between 0 and 1:
respectively recording three-dimensional arithmetic mean deviation of SEM images of the front surfaces of the sheets of the insulation paper boards as Sa1，S2，…Sai…SaσSetting:
obtained by
Is the average roughness, i.e. one of the ageing characteristics of the board.
In step S5, reconstructing a three-dimensional model of the fiber specifically includes:
and reconstructing a fiber three-dimensional model by using a shortest diagonal method.
The fiber three-dimensional reconstruction is characterized in that a triangular mesh model is constructed by using continuous cross section slice images and a shortest diagonal method, a fiber three-dimensional model is reconstructed, and fiber models before and after aging are compared to observe microscopic changes of the fiber after aging.
And reconstructing a three-dimensional model by using the extracted section fiber profile. Due to the problems of the placement position and the focusing of the sample and the like, the difference between the central positions of two adjacent cross section fiber contour lines is large, so that the central points of the adjacent contour lines need to be overlapped and have the same size ratio.
Where Panactor is the translation distance of the Contour, ZoomFactor is the scaling factor, Contour1And Contour2Respectively representing two adjacent contour lines.
And constructing the triangular mesh model by using a shortest diagonal method. Two ends of a contour line segment are connected with one point on the contour line on the adjacent cross section, so that a triangular surface is formed, three-dimensional reconstruction is realized by using a series of contour lines, namely, a series of triangular surface patches are used for connecting the contour lines on two surfaces. Since the contour lines have been preprocessed beforehand, 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 formula:
wherein Qx、QyIs a coordinate value, P, of the current pixel pointx、PyRespectively, the coordinate values closest to the current point.
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 whether the insulating paperboard has aging possibility or not can be preliminarily judged by a direct observation method as shown in fig. 8.
In the step S6, analyzing the microscopic morphology defects of the fibers, and determining the aging degree of the insulating paperboard by combining the inspection criteria of fiber diameter, section porosity and roughness and the three-dimensional model judgment, specifically comprising:
comparing the fiber models before and after aging aiming at the three-dimensional model to observe the microscopic change of the aged fibers 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,
respectively representing the assigned values of fiber diameter, section porosity and roughness, and the aging judgment standard is as follows:
and calculating the aging characteristic value of the unaged paperboard and the aged paperboard, and normalizing the aged paperboard parameters by taking the unaged paperboard parameters as a reference.
According to research and analysis, the diameters of the paper board fibers after aging tend to decrease, the cross section porosity is increased, and the surface roughness is slightly reduced in the initial aging stage due to ablation effect but is gradually increased. And (3) normalizing by taking the unaged paperboard as a reference, and judging that the paperboard begins to age when the fiber diameter of the paperboard is lower than 0.9908, the porosity reaches 1.0785 and the roughness is lower than 1. However, because of errors in the sample parameter detection process, a single characteristic quantity cannot be used as an aging test standard, and the relationship between the fiber diameter, the porosity and the roughness and the aging of the paperboard needs to be comprehensively considered. The specific operation is as follows: 1. assigning scores to the respective characteristic values as shown in table 1; 2. the porosity and the roughness are detected for the whole paperboard, the diameter is a result of measuring the fiber for an individual, certain contingency exists, and according to the proportionality coefficients of 0.4, 0.4 and 0.2, all characteristic values are comprehensively considered to judge whether the paperboard is aged or not.
TABLE 1 assignment criteria for characteristic values
Note: the data in the table are based on the parameters of the unaged paperboard, and the fiber diameter, the porosity and the roughness of the unaged paperboard are all 1.
Judging the aging degree of the paperboard: respectively measuring the characteristic values of the sample to be detected and the unaged paperboard; normalizing each characteristic value by taking the unaged paperboard as a reference; assigning scores to the characteristic values according to table 1; substituting the characteristic value after assigning into formula (19) according to the proportionality coefficients of 0.4, 0.4 and 0.2 to calculate Age,
to be provided with
For the purpose of example, a table look-up is obtained,
calculated by substituting into equation (19)
The aging of the paperboard can be judged.
According to the insulated paperboard aging distinguishing and inspecting method based on SEM image processing, SEM images of insulated paperboards under different aging conditions are obtained through a scanning electron microscope; the fiber profile and the section holes of the insulating paperboard are extracted by adopting an image processing technology, and the fiber diameter and the section porosity can be calculated; calculating roughness and drawing a fiber microscopic three-dimensional topography graph by utilizing the characteristics that the closer particles to a light source in an SEM image, the higher the pixel gray value is, the farther particles are from the light source, and the smaller the gray value is; the change trend of the microscopic characteristic quantity of the paperboard 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 one-step study on the long-term service capability of the paperboard and improvement on the insulating property.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (7)
1. An insulation paperboard aging distinguishing and inspecting method based on SEM image processing is characterized by comprising the following steps:
s1, preparing a plurality of insulating paperboard samples, and acquiring front SEM images and cross-section SEM images 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 preprocessed front SEM image and cross-section SEM image;
s4, calculating the fiber diameter and the section porosity based on the fiber edge image, drawing a fiber micro-morphology three-dimensional graph by using matlab, and calculating the roughness;
s5, repeating the step S1, obtaining a plurality of continuous cross-section SEM images, and performing three-dimensional model reconstruction on the fibers by using fiber edge images extracted from the continuous cross-section SEM images;
and S6, analyzing the micro-morphology defects of the fibers, and judging the aging degree of the insulating paperboard by combining the detection criteria of fiber diameter, section porosity and roughness and the judgment of a three-dimensional model.
2. The method for judging and inspecting the aging of the insulation paperboard based on SEM image processing as claimed in claim 1, wherein the step S1 is to prepare a plurality of insulation paperboard samples, and to obtain the front SEM images and the cross-section SEM images of the insulation paperboard samples by using a scanning electron microscope, and the method specifically comprises the following steps:
cutting a plurality of insulating paper board samples to a certain size, and putting the insulating paper board samples into a scanning electron microscope to obtain SEM images of the front surfaces of a plurality of insulating paper boards;
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 polishing each time, controlling the polishing thickness to be about 10 microns +/-2 microns, and putting into a scanning electron microscope to obtain the SEM image of the cross section of the insulating paperboard.
3. The method for discriminating and inspecting the aging of the insulation paperboard based on the SEM image processing as claimed in claim 1, wherein the step S2 of pre-processing the front SEM image and the cross-sectional SEM image by image enhancement, image de-noising and segmentation method specifically comprises:
the image enhancement selects a method combining piecewise linear enhancement and a histogram equalization algorithm, so that the contrast between the fiber outline and the background in the image is improved, and a high-contrast SEM image is obtained;
the image denoising adopts an adaptive median filtering algorithm.
4. The method for judging and inspecting aging of insulation paperboard based on SEM image processing as claimed in claim 1, wherein in step S3, the extracting fiber edge images in the pre-processed front SEM image and cross-section SEM image respectively comprises:
adopting a canny algorithm to construct five-step degree templates of 8 directions of central pixels of 0 degrees, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees and 157.5 degrees, and respectively calculating first-order partial derivative finite differences, gradient amplitudes and gradient directions of all the directions;
after a partial derivative matrix is solved, non-maximum suppression is carried out on the gradient amplitude, the peak-valley value of a gray level histogram is selected to be set as the initial height, the gradient amplitude is binarized by a low dual threshold value, edge extraction is carried out on the gradient amplitude, the dual threshold values are adjusted according to the extraction result, and finally a fiber edge image is obtained.
5. The method for judging and inspecting the aging of the insulating paperboard based on the SEM image processing according to claim 1, wherein in the step S4, the fiber diameter and the cross-sectional porosity are calculated based on the fiber edge image, and a three-dimensional image of the fiber micro-morphology is drawn by using matlab to calculate the roughness, specifically comprising:
calculating the fiber diameter: measuring the diameters of theta fibers in the extracted edge images by using a matlab tool kit, and using the drawn diameter frequency distribution diagram and a normal distribution fitting curve as aging judgment bases; meanwhile, the peak value of the fitted curve corresponds to the diameter l1As a center,. lkThe diameter is increased to be in the range of1±lkThe number of fibers in the range is 75% of the total number of fibers detected, and the diameter is l1±lkFibers within the range are defined as the average diameter of the group of fibers represented by the paperboard
As a board aging characteristic value;
calculating the section porosity: taking a pixel point as a minimum area unit, calculating the number of pixels surrounded by the hole edges between the fiber outlines in the extracted cross-section image, namely the total hole area of the cross-section is recorded as S, and comparing the total hole area with the area S of the total area of the image to obtain the porosity
Calculating the porosity of all the obtained sectional images, namely the calculated porosity is gamma1，γ2，γ3……γτGet the average value
Is the porosity of the cross section of the fiber;
calculating the roughness: using the characteristic that the closer the particles to the light source in the SEM image, the higher the pixel gray value, the farther the particles are from the light source, and the smaller the gray value, and using the gray value of each pixel as the height to represent the fiber micro-morphology three-dimensional image of the image;
normalizing the gray value of the SEM image, and calculating the average gray value of the normalized gray image:
where M and N are the number of discrete sampling points in the x-direction and y-direction, respectively, i.e., the X, Y value of the image, F (x)i，yj) Normalizing to grey scale value between 0 and 1, and calculating the average deviation S of the surface roughness of the insulating paperboard in three dimensionsaAnd (3) characterization:
respectively recording three-dimensional arithmetic mean deviation of SEM images of the front surfaces of the sheets of the insulation paper boards as Sa1，S2，…Sai…SaσSetting:
obtained by
The average roughness is.
6. The method for judging and inspecting the aging of the insulation paperboard based on the SEM image processing as claimed in claim 1, wherein the step S5 of reconstructing the three-dimensional model of the fiber comprises:
and reconstructing a fiber three-dimensional model by using a shortest diagonal method.
7. The SEM image processing-based insulation board aging distinguishing and inspecting method as claimed in claim 5, wherein in the step S6, the analyzing of the micro-morphology defects of the fibers and the distinguishing of the aging degree of the insulation board by combining the inspection criteria of fiber diameter, section porosity and roughness and the three-dimensional model judgment specifically comprises the following steps:
comparing the fiber models before and after aging aiming at the three-dimensional model to observe the microscopic change of the aged fibers 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,
respectively representing the assigned values of fiber diameter, section porosity and roughness, and the aging judgment standard is as follows:
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