CN111223098B - Trolley grate inclination angle detection method and system of sintering machine - Google Patents
Trolley grate inclination angle detection method and system of sintering machine Download PDFInfo
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- 238000005245 sintering Methods 0.000 title claims abstract description 74
- 238000001514 detection method Methods 0.000 title abstract description 20
- 238000000034 method Methods 0.000 claims abstract description 69
- 238000007781 pre-processing Methods 0.000 claims abstract description 34
- 238000012545 processing Methods 0.000 claims abstract description 28
- 230000008569 process Effects 0.000 claims description 21
- 238000006243 chemical reaction Methods 0.000 claims description 16
- 230000000877 morphologic effect Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000003745 diagnosis Methods 0.000 abstract description 12
- 238000012423 maintenance Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 11
- 239000000463 material Substances 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 7
- 238000012216 screening Methods 0.000 description 6
- 238000003860 storage Methods 0.000 description 6
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 4
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27B—FURNACES, KILNS, OVENS, OR RETORTS IN GENERAL; OPEN SINTERING OR LIKE APPARATUS
- F27B21/00—Open or uncovered sintering apparatus; Other heat-treatment apparatus of like construction
- F27B21/02—Sintering grates or tables
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D21/02—Observation or illuminating devices
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/26—Measuring arrangements characterised by the use of optical techniques for measuring angles or tapers; for testing the alignment of axes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D2021/0057—Security or safety devices, e.g. for protection against heat, noise, pollution or too much duress; Ergonomic aspects
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27M—INDEXING SCHEME RELATING TO ASPECTS OF THE CHARGES OR FURNACES, KILNS, OVENS OR RETORTS
- F27M2003/00—Type of treatment of the charge
- F27M2003/04—Sintering
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Abstract
The application discloses a trolley grate bar inclination angle detection method of a sintering machine, which comprises the following steps: acquiring initial complete images of all grate bars on a trolley of a sintering machine; image preprocessing is carried out on the initial complete images of all the grate bars to obtain images with sharp edges of all the grate bars; dividing the image with sharp edges of all the grate bars according to a preset processing strategy to obtain an image with sharp edges of one grate bar; the sharp image of the edge straight line of the grate bars is a collection of the edge straight line image formed by each grate bar of the row; obtaining the slope of an edge straight line of the edge straight line image formed by each grate bar; and alarming when the slope of the edge straight line is larger than or equal to a preset slope threshold value. The method can conveniently and accurately detect the inclination angle and total number of the grate bars, grasp the inclination and number conditions of the grate bars, perform fault diagnosis and the like and take corresponding maintenance measures.
Description
Technical Field
The application relates to the technical field of sintering machines, in particular to a trolley grate bar inclination angle detection method and system of a sintering machine.
Background
The sintering is a process that various powdery iron-containing raw materials are mixed with a proper amount of fuel, solvent and water, and then are sintered on equipment after being mixed and pelletized, so that a series of physical and chemical changes are generated on the materials, and mineral powder particles are bonded into blocks. The sintering operation is a central link of sintering production, and comprises main working procedures of material distribution, ignition, sintering and the like, and key equipment in the sintering operation is a sintering machine. Referring to fig. 1, fig. 1 is a schematic structural diagram of a sintering machine in the prior art.
As shown in fig. 1, the sintering machine includes a pallet 101, a hearth layer bin 102, a sintering mixture bin 103, an ignition furnace 104, a head star wheel 105, a tail star wheel 106, a sinter breaker 107, a wind box 108, an exhaust fan 109, and the like. The belt sintering machine is sintering mechanical equipment which is driven by a head star wheel and a tail star wheel and is provided with a trolley filled with mixture and an ignition and exhaust device. The trolleys are continuously operated on a closed track in an end-to-end manner, as in fig. 1, the upper and lower layers of tracks are fully paved with the trolleys, and one sintering machine comprises hundreds of trolleys. After the iron-containing mixture is fed onto the trolley through the feeding device, the ignition device ignites the surface material, a series of bellows are arranged below the bottom of the trolley, one end of each bellows is a large exhaust fan, and the materials filled in the trolley are gradually combusted from the surface to the bottom of the trolley through exhaust.
The grating bars are paved on the trolley. The grate bar of the sintering machine is used as an important component part of the trolley, and the grate bar can cause the conditions of material leakage, poor air permeability and the like after failure, so that the state of the grate bar directly influences the normal operation of the sintering production and the sintering quality. The grate bars are fixed on the trolley cross beams and are used for bearing materials and guaranteeing the air permeability of sintering reaction. Because the sintering trolley runs continuously for 24 hours, the grate bars are easy to damage under the actions of mineral weight, negative air draft pressure and repeated high temperature, and adverse effects caused by the damage of the grate bars are as follows:
the first grate bar is missing. After the grate bars are broken and fall off, the gap width of the single-row grate bars can be increased, and when the gap is too large, the sintered mixture can fall into the flue from the gap holes, so that the material surface forms a rat hole.
2) The grate is inclined. The inclination degree of the grate bars is influenced by the abrasion and the deletion of the grate bars, and when the grate bars are excessively inclined, the grate bars cannot be clamped on the trolley body, so that large-area falling off is formed.
3) The gaps of the grate bars are stuck. The sintering mineral aggregate is blocked in the gaps of the grate bars, and the large-area blockage leads to poor air permeability of the sintering reaction and influences the quality of the sintering mineral.
Disclosure of Invention
The technical problem to be solved by the application is to provide the trolley grate bar inclination angle detection method of the sintering machine, which can conveniently and accurately detect the inclination angle of the grate bar, grasp the inclination condition of the grate bar, and position the grate bar with overlarge inclination angle so as to perform fault diagnosis and the like and take corresponding maintenance measures. In addition, the method can conveniently and accurately detect the total number of the bars, grasp the missing condition of the bars, locate the missing position of the bars, and perform fault diagnosis and the like and take corresponding maintenance measures according to the missing condition of the bars. Furthermore, another technical problem to be solved in the present application is to provide a trolley grate inclination angle detection system of a sintering machine.
In order to solve the technical problems, the application provides a method for detecting the inclination angle of a grate bar of a trolley of a sintering machine, which is used for detecting the inclination angle of the grate bar on the trolley of the sintering machine and comprises the following steps:
acquiring initial complete images of all grate bars on a trolley of a sintering machine;
image preprocessing is carried out on the initial complete images of all the grate bars to obtain images with sharp edges of all the grate bars;
dividing the image with sharp edges of all the grate bars according to a preset processing strategy to obtain an image with sharp edges of one grate bar; the sharp image of the edge straight line of the grate bars is a collection of the edge straight line image formed by each grate bar of the row;
obtaining the slope of an edge straight line of the edge straight line image formed by each grate bar;
and alarming when the slope of the edge straight line is larger than or equal to a preset slope threshold value.
Optionally, the process of obtaining the slope of the edge straight line image formed for each grate bar includes:
aiming at an edge linear image formed by a grate bar, a linear fitting algorithm is adopted to obtain the edge linear of the grate bar: y is i ＝k i x i +b i Coordinates of the two endpoints: p is p i1 (x i1 ,y i1 )、p i2 (x i2 ,y i2 ) At this time, i represents a fitting straight line corresponding to an ith grate bar in the current grate bars;
the absolute value of the slope of the edge line of the grate is calculated by the following formula:
will k i And selecting an edge straight line corresponding to the slope larger than or equal to 1 to obtain the slope of the corresponding edge straight line.
Optionally, the detection method further includes: in x 11 As the abscissa corresponding to the fitting straight line of the first grate, the adjacent interval deltax=x is reserved i2 -x i1 Fitting straight lines larger than a set interval threshold value to obtain a slope vector K of the grate bars i In this case, i represents the number of rows of the grate bars.
Optionally, the detection method further includes:
calculating the slope vector of each row of grate bars, and correspondingly storing the slope of each grate bar according to the following matrix formula:
wherein,n i the number of the gradients of the grating bars detected by the ith row of grating bars is represented.
Optionally, the detection method further includes:
the number of the grate bars in each row is equal to the slope number of the row, and the formula is as follows:
num 1 ＝n 1 ；
num 2 ＝n 2 ；
num 3 ＝n 3 ；
the number of the grate bars is stored as follows: n= [ num ] 1 ,num 2 ,num 3 ]。
Optionally, the detection method further includes:
setting delta 1 Represents a first slope threshold, delta 2 Represents a second slope threshold and delta 1 ＜δ 2 ；
When num is 1 ＝N Fixing device In the time-course of which the first and second contact surfaces,
and Max (K) 1 )<δ 1 When the alarm signal is not sent out;
δ 1 ＝<Max(K 1 )<δ 2 When the alarm signal is sent out, a lightweight alarm signal is sent out;
Max(K 1 )>＝δ 2 when the system is in a normal state, sending out a heavyweight alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
Optionally, the detection method further includes:
setting delta 1 Represents a first slope threshold, delta 2 Represents a second slope threshold and delta 1 ＜δ 2 ；
When N is Fixing device -num 1 ＝<In the case of 2, the number of the times of the operation,
and Max (K) 1 )<δ 1 When the first light alarm signal is sent out;
δ 1 ＝<Max(K 1 )<δ 2 when the system is in a first state, sending out a first light-level alarm signal;
Max(K 1 )>＝δ 2 when the system is in a normal state, sending out a heavyweight alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
Optionally, the detection method further includes:
setting delta 1 Represents a first slope threshold, delta 2 Represents a second slope threshold and delta 1 ＜δ 2 ；
When 2<N Fixing device -num 1 ＝<In the time of 4, the time of the process,
and Max (K) 1 )<δ 1 When the alarm signal is sent out, a lightweight alarm signal is sent out;
δ 1 ＝<Max(K 1 )<δ 2 when the system is in a first heavyweight level alarm signal;
Max(K 1 )>＝δ 2 when the system is in a first state, sending out a first heavy-level alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
Optionally, the detection method further includes:
when N is Fixing device -num 1 >4, sending out a heavyweight alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
Alternatively to this, the method may comprise,
the process for preprocessing the initial complete images of all the rows of the grate bars to obtain the images with sharp straight lines at the edges of all the rows of the grate bars comprises the following steps:
Performing primary image preprocessing of gray level conversion on the obtained initial complete images of all rows of the grating bars to obtain gray level images of all rows of the grating bars;
and carrying out second image preprocessing based on the gray level image to obtain images with sharp edge lines of all rows of grate bars.
Optionally, the second image preprocessing includes:
based on the gray level image, performing binarization processing to obtain binarization images of all grate bars;
and carrying out third image preprocessing based on the binarized image to obtain images with sharp edge lines of all rows of grate bars.
Optionally, the third image preprocessing includes:
based on the binarized image, morphological algorithm processing is carried out to obtain images with sharp edge lines of all grate bars.
Optionally, the process of dividing the image with sharp edges of all the grate bars according to a preset processing strategy to obtain the image with sharp edges of one grate bar includes:
determining dividing points of the image with sharp straight lines at the edges of all rows of the bars in the length direction of the bars according to the number of rows of the original known bars and the length of a single bar;
Then dividing the image with sharp edge lines of all rows of the bars into the image with sharp edge lines of one row of the bars by the straight lines which pass through the dividing points and are perpendicular to the bars.
In addition, in order to solve the above technical problem, the present application further provides a trolley grate bar inclination angle detection system of a sintering machine, for detecting an inclination angle of a grate bar on a trolley of the sintering machine, comprising:
the acquisition unit is used for acquiring initial complete images of all grate bars on the trolley of the sintering machine;
the preprocessing unit is used for preprocessing the initial complete images of all the grate bars to obtain images with sharp edges of all the grate bars;
the dividing unit is used for dividing the images with sharp edge lines of all the grate bars according to a preset processing strategy to obtain images with sharp edge lines of one grate bar; the sharp image of the edge straight line of the grate bars is a collection of the edge straight line image formed by each grate bar of the row;
the computing unit is used for obtaining the slope of the edge straight line image formed by each grate bar;
and the alarm unit is used for alarming when the slope of the edge straight line is greater than or equal to a preset slope threshold value.
In one embodiment, the method for detecting the inclination angle of the grate bar of the trolley of the sintering machine provided by the application is used for detecting the inclination angle of the grate bar on the trolley of the sintering machine and comprises the following steps: acquiring initial complete images of all grate bars on a trolley of a sintering machine; image preprocessing is carried out on the initial complete images of all the grate bars to obtain images with sharp edges of all the grate bars; dividing the image with sharp edges of all the grate bars according to a preset processing strategy to obtain an image with sharp edges of one grate bar; the sharp image of the edge straight line of the grate bars is a collection of the edge straight line image formed by each grate bar of the row; obtaining the slope of an edge straight line of the edge straight line image formed by each grate bar; and alarming when the slope of the edge straight line is larger than or equal to a preset slope threshold value. The method can conveniently and accurately detect the inclination angle of the grate, grasp the inclination condition of the grate, and position the grate with overlarge inclination angle so as to perform fault diagnosis and the like and take corresponding maintenance measures. In addition, the method can conveniently and accurately detect the total number of the bars, grasp the missing condition of the bars, locate the missing position of the bars, and perform fault diagnosis and the like and take corresponding maintenance measures according to the missing condition of the bars.
Drawings
FIG. 1 is a schematic diagram of a sintering machine according to the prior art;
FIG. 2 is a functional block diagram of a method for detecting the inclination angle of a grate bar of a sintering machine according to one embodiment of the present application;
FIG. 3 is a schematic view of a part of the structure of a sintering machine in the present application;
FIG. 3-1 is a logic flow diagram of a method for detecting the inclination angle of a grate bar of a sintering machine in one embodiment of the present application;
FIG. 4 is a schematic view of an initial complete image of a grate bar obtained in a method for detecting the inclination angle of a trolley grate bar of a sintering machine according to an embodiment of the present application;
FIG. 5 is a binarized image obtained by binarizing the image of FIG. 4;
FIG. 6 is an image obtained by morphological processing of the image of FIG. 5;
FIG. 7 is a mounting block diagram of a grate bar of the sintering machine of FIG. 3;
FIG. 8 is a view showing various inclined structures of a grate bar of the sintering machine of FIG. 3;
FIG. 9 is a schematic view of coordinates of two points on the inclined grate bar of FIG. 8;
FIG. 10 is a schematic view showing a structure of the sintering machine of FIG. 3 during screening of a grate bar of a trolley;
FIG. 11 is a flowchart of a method for detecting the inclination angle of a grate bar of a sintering machine according to an embodiment of the present invention.
Detailed Description
In order to enable those skilled in the art to better understand the present invention, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present invention with reference to the accompanying drawings.
In some of the flows described in the specification and claims of the present invention and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.
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 fall within the scope of the invention.
Referring to fig. 2 for the system functional structure of the present application, fig. 2 is a functional block diagram of a method for detecting a grid inclination angle of a sintering machine according to an embodiment of the present application.
As shown in fig. 2, the functional modules include an image acquisition device, data and model storage, image acquisition, parameter output, feature parameter calculation, intelligent diagnosis model, state output, and the like. The image acquisition device is used for preprocessing the acquired image and storing the preprocessed image into the data and model storage module. The data and model store and output the grate image to the image acquisition module, and output the characteristic parameters to the parameter acquisition module. Parameters in the feature parameter calculation model are also stored in the data and model storage module.
The image acquisition device can specifically refer to fig. 3, and fig. 3 is a schematic diagram of a part of the structure of the sintering machine in the application.
(1) Image acquisition device
The invention installs a set of image acquisition device at the upper layer maintenance platform of the machine head, the structure is shown in figure 3, and the device comprises a camera 201, a light source 202 and a mounting bracket, and is used for acquiring the image of the grate bar on the trolley 203. And selecting one or more proper cameras for acquisition according to the size of the field of view, the lens parameters, the camera parameters and the like. Fig. 3 shows an example of synchronous acquisition of grate images by two cameras.
(2) Image acquisition:
the two cameras adopted by the device synchronously acquire images, each camera is divided into a left side and a right side, each camera is responsible for a part of area, the field areas of view of the cameras are overlapped to a certain extent and are used for image stitching, and an image stitching algorithm such as SIFT, SURF and the like is adopted to stitch the images on the left side and the right side to combine the images on the left side and the right side into a complete grate image at the bottom of the trolley. Referring to fig. 4, fig. 4 is a schematic diagram of an initial complete image of a grate obtained in a method for detecting a grate inclination angle of a trolley of a sintering machine according to an embodiment of the present application;
the acquired image data is managed in a storage system.
(3) And (3) calculating a characteristic parameter calculation model:
as can be seen from fig. 4, three rows of bars are arranged at the bottom of each trolley, the bars are in a strip-shaped structure, are closely arranged on the trolley body, a gap is formed between every two adjacent bars, and the characteristics of the gap area and the bar body area in the acquired image are different.
The model is used for calculating the number of each row of the grates in the grate image, and the processing process is as follows:
1. gray level conversion and binary conversion to obtain a binary image of the grating, wherein a white area is a grating area, and a black area is a grating gap area. The binarization processing can reduce interference of uneven illumination on outline extraction, and the obtained binarized image is specifically referred to fig. 5, and fig. 5 is a binarized image obtained by performing binarization processing on the image in fig. 4. As a comparison of fig. 5 and 4, the comparison between the grate bars and the gaps is more apparent in fig. 5.
The following describes the gray scale transformation in this application in detail.
Gray level transformation: the gray level conversion is to convert the image obtained by the camera into a gray level image, for example, a color camera is adopted, one pixel of the obtained image is represented by three color components of red, green and blue, the obtained image is a three-channel image (R, G, B), after the gray level conversion, each pixel is represented by a gray level value, and the value range of the gray level value is 0-255, and the obtained image is changed into a single-channel image. The conversion method comprises the following steps:
1): averaging method-averaging the RGB values of 3 channels at the same pixel location
l(x,y)＝1/3*l_R(x,y)+1/3*l_G(x,y)+1/3*l_B(x,y)
2) Maximum minimum average method-taking average of maximum and minimum brightness in RGB of same pixel position
l(x,y)＝0.5*max(l_R(x,y),l_G(x,y),l_B(x,y))+0.5*min(l_R(x,y),l_G(x,y),l_B(x,y))
3) Weighted averaging-the weights before each color channel are not the same, e.g., 0.3 x r+0.59 x g+0.11 x b.
It should be noted that the above gray scale conversion method is only an example, and it is obvious that other gray scale conversion methods can achieve the purposes of the present application and should be within the scope of the present application.
The following describes the binary processing of the present application:
the gray level image is 0-255, and the binary image can be called black-and-white image, wherein the value 0 represents black and 255 represents white, a threshold value T is generally set when binary conversion is performed, when the gray level value of a certain pixel point is greater than T, the value of the pixel point is set to 255, and when the gray level value is less than T, the value of the pixel point is set to 0.
In the above description, it should be noted that the above binary processing method is only an example, and it is obvious that other binary processing methods can achieve the purposes of the present application and should be within the scope of protection of the present application.
After the gradation conversion and the binary processing are completed, the following processing is then performed on the image:
2. morphological treatment: in fig. 5, there are some black small points on the grate area, which can make the extracted grate outer contour discontinuous, in order to reduce the interference of black noise points in the grate area on the extraction of grate edge contour, the image is processed by open operation, close operation, morphological gradient, top cap and black cap algorithm in morphological filtering, so as to obtain a clean grate area image, as shown in fig. 6, fig. 6 is an image obtained by morphological processing of the image in fig. 5, and the black noise points are less than fig. 5.
As an example, the open operation, the close operation, the morphological gradient, the top hat and the black hat algorithm used in the present application are specifically described below.
The start-up operation is a corrosion-then-expansion operation, which aims to separate two objects that are finely linked together.
The close operation is an expansion-then-erosion operation, whose purpose is to join two finely connected image blocks together.
The erosion may "thin" the target area, essentially causing shrinkage of the image boundaries, to eliminate small and meaningless targets.
The expansion may "enlarge" the target area, expand the target boundary outward, and incorporate the background points to which the target area is exposed into the target area, which may be used to fill in certain voids in the target area and eliminate small particle noise contained in the target area.
Morphological gradients are also a method of expanding and corroding the appropriate combination of basic operations.
Top cap operation: is the difference between the original image and the result of the open operation.
The black cap operation is the difference between the closed operation and the original image.
It should be noted that the above algorithm is an example of removing some noise points in the binary image by combination of dilation and erosion in the present application, and is not limited to the specific use of dilation or erosion, and it is intended that such a method be utilized for removing noise. It is apparent that other algorithms or combinations of algorithms, if any, are also capable of removing noise, should be within the scope of the technical concepts of the present application.
After morphological processing is performed on the image, the following steps are further needed:
3. edge straight line fitting: dividing the grate bar into upper, middle and lower rows to obtain three sub-images 1 ，image 2 ，image 3 A straight line fitting algorithm, such as Hough straight line fitting, is adopted to obtain a straight line y of the grate edge by utilizing the pixel difference between the grate edge and the gap i ＝k i x i +b i Two end point coordinates p i1 (x i1 ,y i1 )，p i2 (x i2 ,y i2 ) At this time, i represents the ith fitting straight line in the current grate bar.
In this step, as an example, a specific description is made of hough straight line fitting:
hough line detection is to transform a line in an image space to a point in a parameter space, and solve the detection problem through statistical characteristics, as shown in the following figure: three coordinate points are arranged in the Cartesian coordinate system, a straight line fitting the three points is found, the straight line can be converted into an intersection point of the straight line in the parameter space (slope and intercept space), one point in the Cartesian coordinate is converted into a straight line in the parameter space, the number of the straight lines of the intersection points is increased, and the straight line of the parameter values (k, q) represented by the intersection points in the Cartesian is the straight line of the third point.
When the straight line passing through the three points is vertical to the x axis, the straight line is three parallel straight lines after going to the parameter space, so that a polar coordinate mode is generally adopted as the parameter space later.
The problem of detecting straight lines in image space translates to the problem of finding the maximum number of sinusoids passing through a point (r, θ) in polar parameter space.
The general procedure for detecting straight lines using hough transform may be:
1) Conversion of colour images into grey scale images
2) Denoising method
3) Edge extraction
4) Binarization
5) Mapping to Hough space
6) Taking local maximum value, setting threshold value, filtering interference straight line
7) Drawing straight line and calibrating angular point
In this application, the process flow is different from the above. When the Hough straight line detection is carried out, only the processed binary image is needed to be used as a parameter input, and two end point values (x 1, y1, x2 and y 2) of each straight line can be obtained through output, wherein (x 1, y 1) represents the start point of a line segment, and (x 2, y 2) represents the end point of the line segment.
After the image is subjected to edge straight line fitting, the following steps are needed to be carried out:
referring specifically to fig. 8 and 9, fig. 8 is a view showing various inclined structures of a grate bar of a trolley of the sintering machine of fig. 3; fig. 9 is a schematic diagram of coordinates of two points on the inclined grate bar of fig. 8.
4. Calculating the inclination angle: the long sides and the short sides (namely the long sides and the short sides of the rectangle in the aspect of fig. 8 and 9) of the grate bar exist, and the long sides can more clearly reflect the inclination degree of the grate bar, so that the straight lines with the non-conforming slope are required to be removed.
The slope of the line is calculated as:if k is i >And 1, a fitting straight line with long sides is selected to be larger than 1. In the obtained slope, each grate bar Two slopes are included, so that further screening is needed to ensure that the number of slopes is consistent with the number of grates.
And (3) calculating: Δx=x i2 -x i1 In x 11 Starting as a fitting straight line of the first grate, retaining the fitting straight line with the adjacent interval deltax larger than a set threshold value so as to obtain a slope vector K of the grate i In this case, i represents the number of rows of the grate bars.
The slope of the three rows of grids is obtained after the calculation of the inclination angle, the slope is taken as a trolley unit, and the slope of each grid is correspondingly stored in a matrix form, as follows:
wherein,
n i the number of the gradients of the grating bars detected by the ith row of grating bars is represented.
In the above technical solution, further supplementary explanation may be made as follows:
the structure position of the grate bars is shown in figure 8 and is in a long strip shape, and each grate bar rectangle comprises two long sides and two short sides. In hough transform straight line detection, straight lines of long sides and short sides are detected, and in long sides and short sides, the long sides can reasonably reflect the inclination of the grate, so that in the application, the inclination of the grate is represented by the inclination of the rectangular long sides, and the inclination of the grate is shown in fig. 8.
The inclination angle is calculated by selecting two points on the long side, as shown in FIG. 9, A, B Calculating its slope from two points
When the slope is equal to 1, the inclination angle is 45 degrees, and the grate bar is slightly inclined in the vertical direction and the left or right direction and cannot be inclined by more than 45 degrees, otherwise, the short side of the grate bar is smaller than 1. It is thus necessary to save the straight line having a slope greater than 1 by screening while removing the straight line passing through the short side.
For a slope screening strategy, the following can also be made:
referring specifically to fig. 10, fig. 10 is a schematic diagram illustrating a structure of the sintering machine in fig. 3 during screening of the grate bars.
As shown in fig. 10, the grating is of a certain width, so that two straight lines have a certain interval in the x-axis direction, Δx=x i2 -x i1 Selecting a first straight line, reserving a straight line with a reserved distance larger than a certain threshold value, taking green as a reserved straight line in the following chart, selecting a first straight line, selecting a second straight line with a distance larger than the threshold value from the first straight line, and taking a distance between a third straight line and the second straight line larger than the threshold value.
Through slope screening, each grating can only store one inclination angle value, the range of the threshold is set according to the width of the grating, and under the condition that the camera is fixedly installed and the adopted grating size is fixed, the number of pixels occupied by the grating width in an image is within a certain range, and the threshold is set according to the grating width.
5. And (3) root number calculation:
the number of the grate bars=the number of the inclined grate bars, so that the number of the grate bars in each row can be calculated as follows:
num 1 ＝n 1
num 2 ＝n 2
num 3 ＝n 3
and (5) storing the number of the grate bars: n= [ num ] 1 ,num 2 ,num 3 ]
The above are five steps of the parameter calculation model. After the parameter calculation model is completed, the following stages are then entered:
then, intelligent diagnosis is performed, and the following description is made:
referring to fig. 7, fig. 7 is a view showing an installation structure of a grate bar of the sintering machine of fig. 3.
As shown in fig. 7, one trolley unit includes a trolley body beam, a heat insulation pad, and a grate bar placed on the heat insulation pad. As shown in figure 7, the bars are movably clamped on the trolley, and the gaps among the bars are small in normal state, and the bars are mutually supported and vertically arranged. When the grid is in fault, the gaps of the grid are enlarged, the grid can move on the trolley beam and can incline to a certain degree after being mutually unsupported, so that the grid fault can be diagnosed according to the inclination angle of the grid. Because the length of the heat insulation pad hooked at the lower end of the grate bar is shorter, when the inclination angle is large, the grate bar of the whole row can fall off in a large area.
In the diagnosis process, please refer to fig. 11, fig. 11 is a flowchart of a more refined method for detecting the inclination angle of the grate bar of the sintering machine according to an embodiment of the present application.
And acquiring a grate inclination angle matrix, then respectively calculating the maximum value of inclination angles in each row, judging whether the maximum inclination angle in each row is greater than a threshold value, and if not, judging that the grate is normal. If so, recording the fault position according to the line number and the serial number value of the maximum inclination angle.
The diagnosis is performed solely for the inclination angle parameter, and the number of the grating bars is not considered. When the inclination angle parameter and the grate root number parameter are combined together for diagnosis, the following settings are needed:
the inclination angle is two-stage, delta 1 And delta 2 Wherein delta 1 ＜δ 2 . During initial operation, the number N of the grate bars of each row of each trolley is set Fixing device 。
Taking the first grate diagnosis as an example:
the second and third rows are diagnosed in the same way as above.
After the fault is detected, the fault position can be positioned according to the interval storage matrix, and the inspection personnel is guided to maintain.
The above table can be described as follows:
setting delta 1 Represents a first slope threshold, delta 2 Represents a second slope threshold and delta 1 ＜δ 2 ；
When num is 1 ＝N Fixing device In the time-course of which the first and second contact surfaces,
and Max (K) 1 )<δ 1 When the alarm signal is not sent out;
δ 1 ＝<Max(K 1 )<δ 2 when the alarm signal is sent out, a lightweight alarm signal is sent out;
Max(K 1 )>＝δ 2 when the system is in a normal state, sending out a heavyweight alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
When N is Fixing device -num 1 ＝<In the case of 2, the number of the times of the operation,
and Max (K) 1 )<δ 1 When the first light alarm signal is sent out;
δ 1 ＝<Max(K 1 )<δ 2 when the system is in a first state, sending out a first light-level alarm signal;
Max(K 1 )>＝δ 2 and sending out a heavyweight alarm signal.
When 2<N Fixing device -num 1 ＝<In the time of 4, the time of the process,
and Max (K) 1 )<δ 1 When the alarm signal is sent out, a lightweight alarm signal is sent out;
δ 1 ＝<Max(K 1 )<δ 2 when the system is in a first heavyweight level alarm signal;
Max(K 1 )>＝δ 2 and sending out a second weight level alarm signal.
When N is Fixing device -num 1 >And 4, sending out a heavyweight alarm signal.
The above is a description of the technical solution of the present application in the scenario. The application further describes the specific technical scheme as follows.
Referring to fig. 3-1, fig. 3-1 is a logic flow diagram of a method for detecting a grid inclination angle of a sintering machine according to an embodiment of the present application.
In one embodiment, a method for detecting a gradient angle of a grate bar of a pallet of a sintering machine, for detecting a gradient angle of a grate bar on a pallet of a sintering machine, includes the steps of:
s101, acquiring initial complete images of all grate bars on a trolley of a sintering machine;
s102, performing image preprocessing on initial complete images of all rows of grate bars to obtain images with sharp edges of all rows of grate bars;
s103, dividing the image with sharp edge lines of all the rows of the bars according to a preset processing strategy to obtain an image with sharp edge lines of one row of the bars; the sharp image of the edge straight line of the grate bars is a collection of the edge straight line image formed by each grate bar of the row;
S104, obtaining the slope of an edge straight line of the edge straight line image formed by each grate bar;
s105, alarming when the slope of the edge straight line is larger than or equal to a preset slope threshold value.
The method can conveniently and accurately detect the inclination angle of the grate, grasp the inclination condition of the grate, and position the grate with overlarge inclination angle so as to perform fault diagnosis and the like and take corresponding maintenance measures.
In the above embodiments, further specific designs may be made. For example, the process of obtaining the slope of the edge straight line image formed by each grate bar comprises the following steps:
aiming at an edge linear image formed by a grate bar, a linear fitting algorithm is adopted to obtain the edge linear of the grate bar: y is i ＝k i x i +b i Coordinates of the two endpoints: p is p i1 (x i1 ,y i1 )、p i2 (x i2 ,y i2 ) At this time, i represents the quasi-corresponding ith grate bar in the current grate barsStraight line combination;
the absolute value of the slope of the edge line of the grate is calculated by the following formula:
will k i And selecting an edge straight line corresponding to the slope larger than or equal to 1 to obtain the slope of the corresponding edge straight line.
In the above technical solution, the detection method further includes: in x 11 As the abscissa corresponding to the fitting straight line of the first grate, the adjacent interval deltax=x is reserved i2 -x i1 Fitting straight lines larger than a set interval threshold value to obtain a slope vector K of the grate bars i In this case, i represents the number of rows of the grate bars.
Further, the detection method further comprises the following steps:
calculating the slope vector of each row of grate bars, and correspondingly storing the slope of each grate bar according to the following matrix formula:
wherein,n i the number of the gradients of the grating bars detected by the ith row of grating bars is represented.
In the above embodiments, further specific designs may also be made.
For example, when the slope of the edge line is greater than or equal to a predetermined slope threshold, the process of alerting includes:
and acquiring the maximum slope value of each row in the matrix, alarming when the maximum slope value is greater than or equal to the slope threshold value, and recording the row number and sequence number position of the grate according to the matrix.
In any of the above embodiments, a specific description may be given of the process of image preprocessing.
The image preprocessing is carried out on the initial complete images of all the grate bars to obtain the images with sharp edges of all the grate bars, which comprises the following steps:
performing primary image preprocessing of gray level conversion on the obtained initial complete images of all rows of the grating bars to obtain gray level images of all rows of the grating bars;
And carrying out second image preprocessing based on the gray level image to obtain images with sharp edge lines of all the grate bars.
The second image preprocessing process comprises the following steps:
based on the gray level image, performing binarization processing to obtain binarization images of all the grate bars;
and carrying out third image preprocessing based on the binarized image to obtain images with sharp edge lines of all the grate bars.
The resulting binary image is shown in fig. 5.
The third image preprocessing process comprises the following steps:
based on the binarized image, morphological algorithm processing is carried out to obtain images with sharp edge lines of all grate bars.
The morphologically processed image is shown in fig. 6.
Further, the process of dividing the image with sharp edge lines of all the grate bars according to a preset processing strategy to obtain the image with sharp edge lines of one grate bar comprises the following steps:
determining dividing points of the image with sharp straight lines at the edges of all rows of the bars in the length direction of the bars according to the number of rows of the original known bars and the length of a single bar;
then dividing the image with sharp edge lines of all rows of the bars into the image with sharp edge lines of one row of the bars by the straight lines which pass through the dividing points and are perpendicular to the bars.
In addition, corresponding to the method embodiment, the application also provides a device embodiment.
A system for detecting an inclination angle of a grate bar of a pallet of a sintering machine, for detecting an inclination angle of a grate bar on a pallet of a sintering machine, comprising:
the acquisition unit is used for acquiring initial complete images of all grate bars on the trolley of the sintering machine;
the preprocessing unit is used for preprocessing the initial complete images of all the grate bars to obtain images with sharp edges of all the grate bars;
the dividing unit is used for dividing the images with sharp edge lines of all the grate bars according to a preset processing strategy to obtain images with sharp edge lines of one grate bar; the sharp image of the edge straight line of the grate bars is a collection of the edge straight line image formed by each grate bar of the row;
the computing unit is used for obtaining the slope of the edge straight line image formed by each grate bar;
and the alarm unit is used for alarming when the slope of the edge straight line is greater than or equal to a preset slope threshold value.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Reference throughout this specification to "multiple embodiments," "some embodiments," "one embodiment," or "an embodiment," etc., means that a particular feature, component, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in various embodiments," "in some embodiments," "in at least one other embodiment," or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, components, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, a particular feature, component, or characteristic shown or described in connection with one embodiment may be combined, in whole or in part, with features, components, or characteristics of one or more other embodiments, without limitation. Such modifications and variations are intended to be included within the scope of the present application.
Furthermore, those skilled in the art will appreciate that the various aspects of the invention are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" terminal, "" component, "or" system. Furthermore, aspects of the present application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
It should be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely a specific embodiment of the application to enable one skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for detecting the inclination angle of a grate bar of a trolley of a sintering machine is used for detecting the inclination angle of the grate bar on the trolley of the sintering machine and is characterized by comprising the following steps:
acquiring initial complete images of all grate bars on a trolley of a sintering machine;
image preprocessing is carried out on the initial complete images of all the grate bars to obtain images with sharp edges of all the grate bars;
dividing the image with sharp edges of all the grate bars according to a preset processing strategy to obtain an image with sharp edges of one grate bar; the sharp image of the edge straight line of the grate bars is a collection of the edge straight line image formed by each grate bar of the row;
obtaining the slope of an edge straight line of the edge straight line image formed by each grate bar;
when the slope of the edge straight line is larger than or equal to a preset slope threshold value, alarming;
the image preprocessing is performed on the initial complete images of all the grate bars to obtain images with sharp straight lines at the edges of all the grate bars, and the image preprocessing comprises the following steps:
performing primary image preprocessing of gray level conversion on the obtained initial complete images of all rows of the grating bars to obtain gray level images of all rows of the grating bars;
Performing second image preprocessing based on the gray level image to obtain images with sharp edge lines of all rows of grate bars;
the second image preprocessing process comprises the following steps:
based on the gray level image, performing binarization processing to obtain binarization images of all grate bars;
performing third image preprocessing based on the binarized image to obtain images with sharp edge lines of all rows of grate bars;
the third image preprocessing process comprises the following steps:
based on the binarized image, morphological algorithm processing is carried out to obtain images with sharp edge lines of all grate bars;
the process for dividing the image with sharp edge lines of all the rows of the grate bars according to a preset processing strategy to obtain the image with sharp edge lines of one row of the grate bars comprises the following steps:
determining dividing points of the image with sharp straight lines at the edges of all rows of the bars in the length direction of the bars according to the number of rows of the original known bars and the length of a single bar;
then dividing the image with sharp edge lines of all rows of the bars into the image with sharp edge lines of one row of the bars by the straight lines which pass through the dividing points and are perpendicular to the bars.
2. The method for detecting the inclination angle of a grate bar of a sintering machine according to claim 1, wherein the step of obtaining the inclination of the edge straight line image formed for each grate bar comprises the steps of:
Aiming at an edge linear image formed by a grate bar, a linear fitting algorithm is adopted to obtain the edge linear of the grate bar: y is i ＝k i x i +b i Coordinates of the two endpoints: p is p i1 (x i1 ,y i1 )、p i2 (x i2 ,y i2 ) At this time, i represents a fitting straight line corresponding to an ith grate bar in the current grate bars;
the absolute value of the slope of the edge line of the grate is calculated by the following formula:
will k i And selecting an edge straight line corresponding to the slope larger than or equal to 1 to obtain the slope of the corresponding edge straight line.
3. The method for detecting the inclination angle of the grate bar of the trolley of the sintering machine according to claim 2, wherein the method further comprises: in x 11 As the abscissa corresponding to the fitting straight line of the first grate, the adjacent interval deltax=x is reserved i2 -x i1 Fitting straight lines larger than a set interval threshold value to obtain a slope vector K of the grate bars i In this case, i represents the number of rows of the grate bars.
4. The method for detecting the inclination angle of the grate bar of the trolley of the sintering machine according to claim 3, wherein the method further comprises:
calculating the slope vector of each row of grate bars, and correspondingly storing the slope of each grate bar according to the following matrix formula:
wherein,ni represents the number of the grate slopes detected by the ith row of grate bars.
5. The method for detecting the inclination angle of a grate bar of a sintering machine according to claim 4, further comprising:
The number of the grate bars in each row is equal to the slope number of the row, and the formula is as follows:
num 1 ＝n 1 ；
num 2 ＝n 2 ；
num 3 ＝n 3 ；
the number of the grate bars is stored as follows: n= [ num ] 1 ,num 2 ,num 3 ]。
6. The method for detecting the inclination angle of the grate bar of the trolley of the sintering machine according to claim 5, wherein the method further comprises:
setting delta 1 Represents a first slope threshold, delta 2 Represents a second slope threshold and delta 1 ＜δ 2 ；
When num is 1 ＝N Fixing device In the time-course of which the first and second contact surfaces,
and Max (K) 1 )<δ 1 When the alarm signal is not sent out;
δ 1 ＝<Max(K 1 )<δ 2 when the alarm signal is sent out, a lightweight alarm signal is sent out;
Max(K 1 )>＝δ 2 when the system is in a normal state, sending out a heavyweight alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
7. The method for detecting the inclination angle of the grate bar of the trolley of the sintering machine according to claim 5, wherein the method further comprises:
setting delta 1 Represents a first slope threshold, delta 2 Represents a second slope threshold and delta 1 ＜δ 2 ；
When N is Fixing device -num 1 ＝<In the case of 2, the number of the times of the operation,
and Max (K) 1 )<δ 1 When the first light alarm signal is sent out;
δ 1 ＝<Max(K 1 )<δ 2 when the system is in a first state, sending out a first light-level alarm signal;
Max(K 1 )>＝δ 2 when the system is in a normal state, sending out a heavyweight alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
8. The method for detecting the inclination angle of the grate bar of the trolley of the sintering machine according to claim 5, wherein the method further comprises:
Setting delta 1 Represents a first slope threshold, delta 2 Represents a second slope threshold and delta 1 ＜δ 2 ；
When 2<N Fixing device -num 1 ＝<In the time of 4, the time of the process,
and Max (K) 1 )<δ 1 When the alarm signal is sent out, a lightweight alarm signal is sent out;
δ 1 ＝<Max(K 1 )<δ 2 when the system is in a first heavyweight level alarm signal;
Max(K 1 )>＝δ 2 when the system is in a first state, sending out a first heavy-level alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
9. The method for detecting the inclination angle of the grate bar of the trolley of the sintering machine according to claim 5, wherein the method further comprises:
when N is Fixing device -num 1 >4, sending out a heavyweight alarm signal;
wherein N is Fixing device Representing a set standard number value for each row of grate bars.
10. A system for detecting an inclination angle of a grate bar of a pallet of a sintering machine, for detecting an inclination angle of a grate bar on a pallet of a sintering machine, comprising:
the acquisition unit is used for acquiring initial complete images of all grate bars on the trolley of the sintering machine;
the preprocessing unit is used for preprocessing the initial complete images of all the grate bars to obtain images with sharp edges of all the grate bars;
the dividing unit is used for dividing the images with sharp edge lines of all the grate bars according to a preset processing strategy to obtain images with sharp edge lines of one grate bar; the sharp image of the edge straight line of the grate bars is a collection of the edge straight line image formed by each grate bar of the row;
The computing unit is used for obtaining the slope of the edge straight line image formed by each grate bar;
the alarm unit is used for giving an alarm when the slope of the edge straight line is greater than or equal to a preset slope threshold value;
wherein the preprocessing unit is further configured to:
performing primary image preprocessing of gray level conversion on the obtained initial complete images of all rows of the grating bars to obtain gray level images of all rows of the grating bars;
based on the gray level image, performing binarization processing to obtain binarization images of all grate bars;
based on the binarized image, morphological algorithm processing is carried out to obtain images with sharp edge lines of all grate bars;
determining dividing points of the image with sharp straight lines at the edges of all rows of the bars in the length direction of the bars according to the number of rows of the original known bars and the length of a single bar;
then dividing the image with sharp edge lines of all rows of the bars into the image with sharp edge lines of one row of the bars by the straight lines which pass through the dividing points and are perpendicular to the bars.
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