A method and device for measuring the ring formation and residual thickness of refractory materials in rotary kiln

Abstract

The present invention discloses a method and device for measuring the ringing and residual thickness of refractory materials in a rotary kiln, belonging to the field of iron and steel metallurgy. The method comprises the following steps: scanning the outer wall and inner wall of the rotary kiln with a laser scanner to obtain relevant point cloud data of the rotary kiln; preprocessing the relevant point cloud data of the rotary kiln to obtain point cloud data of the ringing and refractory thickness inside the rotary kiln; using the point cloud data of the ringing and refractory thickness inside the rotary kiln, using the contour data conversion method to calculate the central axis coordinates of the rotary kiln's global point cloud data, thereby obtaining the wall thickness of the rotary kiln; and visualizing the wall thickness information of the rotary kiln by converting the point cloud data between polar coordinates and Cartesian coordinates to obtain a wall thickness distribution cloud map of the rotary kiln, thereby achieving the measurement of the ringing and residual thickness of the refractory materials in the rotary kiln. The method provides a three-dimensional visualization analysis method for rotary kilns.

Description

Technical Field

[0001] The invention belongs to the technical field of iron and steel metallurgy, and relates to a method and a device for measuring the ring formation and the residual thickness of refractory materials in a rotary kiln. Background Art

[0002] As a key industrial equipment, the rotary kiln plays an important role in the high-temperature calcination process of mineral materials such as cement, metallurgy, lime, and ceramics. Its advantages are low cost, high raw material adaptability, and flexible control, so it is widely used in industrial production. However, due to the influence of factors such as raw material properties, operating parameters, and equipment conditions, the inner wall of the rotary kiln often shows the phenomenon of block material consolidation or ring formation, which leads to a series of problems: (1) Increased gravity load: Ring formation will increase the gravity load of the rotary kiln, increase equipment wear and energy consumption. (2) Poor operation of the furnace charge: Ring formation will hinder the smooth operation of the furnace charge, affecting the calcination effect and product quality. (3) Increased production energy consumption: Ring formation will lead to a decrease in heat transfer efficiency and increase production energy consumption. (4) Decreased product quality and output: Ring formation will affect product quality and output, reducing production efficiency. Therefore, extending the ring formation cycle of the rotary kiln has become a common problem faced by related industries. At present, the monitoring of the wall thickness of the rotary kiln mainly adopts soft measurement, that is, by constructing a physical model and combining it with infrared temperature measurement of the outer wall to achieve reverse wall thickness estimation. However, the accuracy of this approach remains questionable. This is primarily due to the fact that constructing a physical model requires consideration of numerous factors, such as raw material properties, operating parameters, and equipment conditions, and the accuracy of the model is limited by the accuracy of these factors. Furthermore, infrared temperature measurement of the outer wall is easily affected by environmental factors and the equipment itself, leading to measurement errors. Furthermore, the internal environment of a rotary kiln is complex, with uneven distributions of parameters such as the temperature and pressure fields, making it difficult to accurately obtain wall thickness data. Existing traditional measurement methods, such as manual measurement, also have numerous shortcomings in efficiency and accuracy, making them difficult to meet actual production needs.

[0003] Laser scanning technology is a technology that uses laser beams to measure distance. It emits a laser beam and receives the laser signal reflected from the target object, calculates the propagation time of the laser beam, and thus obtains the distance information of the target object. Laser scanning technology has the following characteristics: (1) high precision; (2) high efficiency; (3) non-contact. Therefore, this method is widely used in the fields of three-dimensional modeling, topographic mapping, industrial measurement, cultural relics archaeology, etc., but it is currently rarely used in the field of metallurgy. Relying on this technology, the entire rotary kiln can be scanned, and the point cloud data of the inner and outer sides can be obtained at the same time. After direct processing, the contour distribution information of the ring and refractory material in the kiln can be obtained. This rotary kiln wall thickness monitoring method based on three-dimensional laser scanning technology has the following characteristics: (1) Three-dimensional laser scanning technology can obtain the contour distribution of the ring and refractory material inside the rotary kiln with high precision, thereby accurately calculating the wall thickness. (2) Three-dimensional laser scanning technology can quickly obtain a large amount of data and improve monitoring efficiency. (3) Three-dimensional laser scanning technology can visualize the wall thickness data and intuitively display the ring and refractory erosion situation. (4) It can be used as a calibration value for various soft measurement models of rotary kiln ring formation and refractory residual thickness, as well as for the construction of industrial big data.

[0004] When processing existing point cloud data, methods such as CAD screenshots, layer cutting, and manual thickness calculations have numerous drawbacks, including: High subjectivity and large errors: Manual thickness calculation relies on the operator's experience and judgment, and different operators may have different standards for selecting cutting layers and estimating thickness, making it difficult to ensure the repeatability and accuracy of the calculated results. Difficulty processing complex shapes: For objects with complex shapes or irregular surfaces, manual thickness calculations can easily miss details or fail to accurately determine thickness variations, resulting in significant deviations from the actual thickness. Time-consuming and labor-intensive: Manual thickness calculations require layer-by-layer cutting, measurement, and calculation, a tedious process. This is particularly labor-intensive and inefficient when processing large-scale point cloud data. Furthermore, the process of finding the central axis of a point cloud with hundreds of millions of points suffers from the following drawbacks: Traditional fitting methods (such as cylindrical fitting) may not accurately describe the geometric features, resulting in reduced accuracy in central axis extraction. Point cloud data often contains noise points and outliers, which interfere with accurate central axis extraction. For example, methods based on projection or fitting are prone to bias in high-noise environments, resulting in inaccurate central axis extraction. Summary of the Invention

[0005] In order to solve the above problems, the technical solution adopted in this application is as follows: a method for measuring the ring formation and residual thickness of refractory materials in a rotary kiln, comprising the following steps:

[0006] Scan the outer wall and inner wall of the rotary kiln with a laser scanner to obtain relevant point cloud data of the rotary kiln;

[0007] Pre-process the relevant point cloud data of the rotary kiln to obtain the point cloud data of the internal ring and refractory thickness of the rotary kiln;

[0008] Using the point cloud data of the internal ring and refractory thickness of the rotary kiln, the contour data conversion method is used to calculate the coordinates of the central axis of the rotary kiln's full-area point cloud data, and then the wall thickness of the rotary kiln is obtained;

[0009] Through the coordinate conversion measurement of point cloud data between polar coordinates and Cartesian coordinate systems, the wall thickness information of the rotary kiln is visualized, and the wall thickness distribution cloud map of the rotary kiln is obtained, thereby realizing the measurement of the rotary kiln ring and refractory residual thickness.

[0010] Furthermore, the model of the laser scanner used is FARO Focus3D S350P.

[0011] Furthermore, in the process of scanning the outer wall and the inner wall of the rotary kiln by a laser scanner to obtain the relevant point cloud data of the rotary kiln, a target is used as a splicing mark, and the alignment of two adjacent stations should use no less than M same-name points for alignment conversion. After alignment, the residual mean square error of the same-name points should not be greater than the fixed proportion of the mean square error of the points required by the specified accuracy level; the adjacent point clouds are spliced ​​in a relative manner, and after reaching a certain range, the automatic difference evaluation function is used to unify the various stations into the same coordinate system to realize the collection of relevant point cloud data of the rotary kiln.

[0012] Furthermore, the process of preprocessing the rotary kiln related point cloud data is as follows:

[0013] The 3D point cloud data containing only the inner wall contours was filtered out, and the point cloud data was processed using an open source library. The point cloud data was converted into an array and the array was sampled every N points.

[0014] Furthermore, the method of using the point cloud data of the internal ring and refractory thickness of the rotary kiln and using the contour data conversion method to calculate the central axis coordinates of the global point cloud data of the rotary kiln, and then obtaining the wall thickness of the rotary kiln includes the following steps:

[0015] S31: Based on the pre-processed three-dimensional point cloud data of the inner wall contour of the rotary kiln, a straight line passing through the axis of the center of the rotary kiln is determined, and the parameters of the straight line passing through the axis of the center of the rotary kiln are optimized using a minimization method. An objective function is constructed: the sum of the distances from all points in the point cloud to the straight line is minimized. Then, an iterative algorithm method for solving unconstrained nonlinear optimization problems is used to optimize the objective function to obtain the coordinates of the central axis of the rotary kiln;

[0016] S32: Calculating the radial distance between each point in the three-dimensional point cloud data of the inner wall contour of the rotary kiln and the central axis of the rotary kiln;

[0017] S33: Based on the radial distance from each point in the three-dimensional point cloud data of the inner wall contour of the rotary kiln to the central axis of the rotary kiln, the radial distance is converted into wall thickness data according to the outer diameter of the rotary kiln.

[0018] Furthermore, the process of converting the radial distance into wall thickness data according to the outer diameter of the rotary kiln is as follows:

[0019] Along the central axis, the rotary kiln is divided into several sections, each of which is a circular surface;

[0020] Each torus is unfolded into a plane to obtain a series of annular plane graphs; when unfolding, the relative position relationship between the points on the torus is maintained;

[0021] On the unfolded plane, measure the distance from each point to the central axis, which is the wall thickness information of that point.

[0022] Furthermore, the process of measuring the point cloud data coordinate conversion between polar coordinates and Cartesian coordinates is as follows:

[0023] According to the Cartesian coordinates (x, y, z) and the cylindrical coordinates (ρ, θ, φ), the radial distance from any point in the point cloud to the central axis is calculated according to the projection distance of the central axis, and the azimuth and rotation angles are converted into degrees.

[0024] A device for measuring thickness of a rotating body, comprising:

[0025] Acquisition module: Scan the outer and inner walls of the rotating body through a laser scanner to obtain relevant point cloud data of the rotating body;

[0026] Preprocessing module: used to preprocess the point cloud data related to the rotating body to obtain the point cloud data inside the rotating body;

[0027] Calculation module: used for point cloud data inside the rotating body, using the contour data conversion method to calculate the coordinates of the central axis of the full-domain point cloud data of the rotating body, and then obtain the wall thickness of the rotating body;

[0028] Visualization module: It is used to visualize the wall thickness information of the rotating body through the coordinate conversion measurement of the point cloud data between polar coordinates and Cartesian coordinate systems, and obtain the wall thickness distribution cloud map of the rotating body.

[0029] The present invention provides a method and device for measuring the ring formation and residual thickness of refractory materials in a rotary kiln, so as to realize the collection of kiln inner wall thickness data, and at the same time provide a three-dimensional visualization analysis method for the rotary kiln, especially some other measurement methods cannot accurately measure the thickness changes in the entire range.

[0030] This invention improves the accuracy of wall thickness detection during rotary kiln production, standardizes measurement standards, and provides a method for the subsequent construction of standardized industrial big data. This method utilizes Python (a programming language) scientific computing data packages and point cloud processing packages to further process 3D contour point cloud data, constructing 3D point cloud contours of the rotary kiln's inner and outer walls. Contour data conversion methods are then used to determine the coordinates of the central axis of the point cloud, converting the point cloud data into wall thickness information. Using the BFGS (a quasi-Newton method) local search method, the objective function is to minimize the sum of the distances from all data points in the point cloud to a straight line. An initial guess and point cloud data are then passed to determine the central axis position. Using relative point positions and a Cartesian coordinate system to polar coordinate conversion strategy, this method achieves thermodynamic mapping of rotary kiln ring formation and refractory residual thickness, as well as high-precision wall thickness measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0032] Figure 1 is a simplified flow chart of the method;

[0033] Figure 2 is a detailed flow chart of the method;

[0034] Figure 3 This is a schematic diagram of the installation location of the laser scanner;

[0035] Figure 4 This is the planed surface image of the rotary kiln laser scanning point cloud image;

[0036] Figure 5 This is a scan of the inner wall of a rotary kiln laser scanning point cloud;

[0037] Figure 6 This is a cloud diagram of rotary kiln wall thickness distribution. DETAILED DESCRIPTION

[0038] It should be noted that, unless there is any conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0040] Figure 1 is a simplified flow chart of the method;

[0041] Figure 2 is a detailed flow chart of the method;

[0042] A method for measuring the ring formation and residual thickness of refractory materials in a rotary kiln comprises the following steps:

[0043] S1: Scan the outer wall and inner wall of the rotary kiln by a laser scanner to obtain relevant point cloud data of the rotary kiln;

[0044] S2: Preprocessing the relevant point cloud data of the rotary kiln to obtain the point cloud data of the internal ring and refractory thickness of the rotary kiln;

[0045] S3: Using the point cloud data of the internal ring and refractory thickness of the rotary kiln, the contour data conversion method is used to calculate the coordinates of the central axis of the full-area point cloud data of the rotary kiln, and then the wall thickness of the rotary kiln is obtained;

[0046] S4: Through the coordinate conversion measurement of point cloud data between polar coordinates and Cartesian coordinate systems, the wall thickness information of the rotary kiln is visualized to obtain a cloud map of the rotary kiln wall thickness distribution, thereby realizing the measurement of the rotary kiln ring and refractory residual thickness.

[0047] Steps S1 / S2 / S3 / S4 are performed sequentially;

[0048] The laser scanner used is a large-area, high-resolution terrestrial laser scanner, model FARO Focus3DS350P.

[0049] At every 1.5m interval inside the rotary kiln, the laser scanner inner wall point cloud is collected and mapped once, and at every 5m interval outside the rotary kiln, the laser scanner is used to measure once. The specific measurement diagram is as follows Figure 3 As shown;

[0050] The laser scanner is used to scan the outer wall and the inner wall of the rotary kiln, and in the process of obtaining the relevant point cloud data of the rotary kiln, in order to meet the needs of monitoring accuracy and collection efficiency, a target is used as a splicing mark, and the registration of two adjacent stations should use no less than M same-name points for registration conversion. After the registration, the residual mean square error of the same-name points should not be greater than the fixed proportion of the mean square error of the points required by the specified accuracy level; the adjacent point clouds are spliced ​​in a relative manner. After reaching a certain range, the automatic difference evaluation function of the software is used to unify the various stations into the same coordinate system to realize the collection of the relevant point cloud data of the rotary kiln.

[0051] M can be 3; the fixed ratio is 1 / 2;

[0052] Figure 4 This is the planed surface image of the rotary kiln laser scanning point cloud image;

[0053] Figure 5 This is a scan of the inner wall of a rotary kiln laser scanning point cloud;

[0054] The pre-processing of the rotary kiln related point cloud data includes data splicing, outlier removal and reference point splicing;

[0055] The process of preprocessing the rotary kiln related point cloud data is as follows:

[0056] Filter out the 3D point cloud data containing only the inner wall contours and process it using Open3D (an open-source library for processing 3D data). Convert the point cloud data into a NumPy array, use Matplotlib (a plotting library) for plotting, and use the minimize function in SciPy for parameter optimization. Sample the data every N points to reduce the amount of computation. N can be 100, 200, 300, etc.

[0057] The process of filtering out the three-dimensional point cloud data containing only the inner wall contour is as follows:

[0058] First, the point cloud data is segmented, using the geometric characteristics of the cylindrical surface to separate the inner wall point cloud from the rest of the image. Next, noise is filtered, using statistical filtering to remove outliers. Data simplification is then performed, reducing the number of point clouds through uniform sampling (sampling every N points, where N can be 100, 200, 300, etc.). Finally, data verification is performed to ensure that the point cloud fully covers the inner wall and contains no holes.

[0059] The method for calculating the central axis coordinates of the global point cloud data of the rotary kiln by using the point cloud data of the internal ring and refractory thickness of the rotary kiln and adopting the contour data conversion method to obtain the wall thickness of the rotary kiln comprises the following steps:

[0060] S31: Based on the pre-processed three-dimensional point cloud data of the inner wall contour of the rotary kiln, a straight line passing through the axis of the center of the rotary kiln is determined. The parameters of the straight line passing through the axis of the center of the rotary kiln are optimized using a minimization method. An objective function is constructed: the sum of the distances from all points in the point cloud to the straight line is minimized. An iterative algorithm for solving unconstrained nonlinear optimization problems is then used for optimization to obtain the coordinates of the central axis of the rotary kiln.

[0061] S32: Calculate the radial distance from each point in the point cloud data of the internal ring formation and refractory thickness of the rotary kiln to the axis;

[0062] S33: Based on the radial distance from each point to the axis in the point cloud data of the internal ring formation and refractory material thickness of the rotary kiln, the radial distance is converted into wall thickness data according to the outer diameter of the rotary kiln.

[0063] The objective function is to minimize the sum of the distances from all points in the point cloud to the straight line, and the position of the central axis of the rotary kiln is obtained by transmitting the initial guess value of the central axis and the point cloud data.

[0064] The process of measuring the point cloud data coordinate conversion between polar coordinates and Cartesian coordinates is as follows:

[0065] According to the Cartesian coordinates (x, y, z) and the cylindrical coordinates (ρ, θ, φ), calculate the radial distance from any point in the point cloud to the central axis according to the projection distance of the central axis, and convert the azimuth and rotation angle into degrees;

[0066] The process of converting the radial distance into wall thickness data according to the outer diameter of the rotary kiln is as follows:

[0067] Along the central axis, the rotary kiln is divided into several sections, each of which is a circular surface;

[0068] Each torus is unfolded into a plane to obtain a series of annular plane graphs; when unfolding, the relative position relationship between the points on the torus is maintained;

[0069] On the unfolded plane, measure the distance from each point to the central axis, which is the wall thickness information of that point.

[0070] A device for measuring thickness of a rotating body, comprising:

[0071] Acquisition module: Scan the outer and inner walls of the rotating body through a laser scanner to obtain relevant point cloud data of the rotating body; the outer and inner walls of the rotating body are scanned using FARO Focus3D S350P; the specific process refers to the scanning process of the outer and inner walls of the rotary kiln;

[0072] The rotary kiln can be square, regular pentagon or regular hexagon, etc.

[0073] Preprocessing module: used to preprocess the point cloud data related to the rotating body to obtain the point cloud data inside the rotating body;

[0074] The process of pre-processing the point cloud data related to the rotary body to obtain the point cloud data inside the rotary body can refer to the process of obtaining the point cloud data related to the rotary kiln by scanning the outer wall and the inner wall of the rotary kiln with a laser scanner;

[0075] Calculation module: used for point cloud data inside the rotating body, using the contour data conversion method to calculate the coordinates of the central axis of the full-domain point cloud data of the rotating body, and then obtain the wall thickness of the rotating body;

[0076] The point cloud data inside the rotary body is converted into the central axis coordinates of the entire point cloud data of the rotary body by using the contour data conversion method, and the wall thickness of the rotary body is obtained by referring to the point cloud data of the internal ring and refractory thickness of the rotary kiln, and the central axis coordinates of the entire point cloud data of the rotary kiln are calculated by using the contour data conversion method, and the wall thickness of the rotary kiln is obtained.

[0077] Visualization module: used to visualize the wall thickness information of the rotating body by measuring the coordinate conversion of point cloud data between polar coordinates and Cartesian coordinates, and obtain a cloud map of the wall thickness distribution of the rotating body. The process of visualizing the wall thickness information of the rotating body by measuring the coordinate conversion of point cloud data between polar coordinates and Cartesian coordinates and obtaining a cloud map of the wall thickness distribution of the rotating body is similar to the process of visualizing the wall thickness information of the rotary kiln by measuring the coordinate conversion of point cloud data between polar coordinates and Cartesian coordinates and obtaining a cloud map of the wall thickness distribution of the rotary kiln;

[0078] Example 1: In this example, a demonstration measurement experiment was conducted on the production line of a pelletizing rotary kiln in a steel company. The overall measurement process can be found in the attached figure. Figure 1 .

[0079] Based on the measurement conditions, dimensions, and accuracy requirements of the rotary kiln, the FARO Focus3D S350P high-precision terrestrial laser scanner was used. The distribution of refractory rings within the kiln was investigated by entering the kiln. Taking into account the kiln's length and lighting conditions, scans were performed every 2 meters inside the kiln, lasting 5 minutes each. Outside the kiln, scans were performed every 5 meters, lasting 5 minutes each.

[0080] To meet the requirements of monitoring accuracy and acquisition efficiency, it is advisable to deploy targets as splicing markers. When using targets for data registration, the registration of two adjacent stations should be transformed using no fewer than three homonymous points. After registration, the residual mean square error of the homonymous points should be no greater than 1 / 2 of the required point mean square error for the specified accuracy level. Secondly, perform preliminary preprocessing on the acquired point cloud data. This can be done manually or using simple software functions to select and delete points. Manual removal is recommended.

[0081] After pre-processing and splicing the point cloud data of the rotary kiln laser scanning, the three-dimensional contour distribution map of the kiln inner wall / outer wall can be obtained with the help of three-dimensional visualization software, as shown in the attached figure. Figure 2 As shown. Figure 2 Laser scanning can accurately capture the contours of kiln rings and refractory materials, but it's difficult to directly measure their thickness based on the 3D images. Since the raw data from rotary kiln 3D laser scanning is a point cloud formatted as (x, y, z, r, p, g), where x, y, and z represent the 3D coordinates in space, and r, p, and g represent the color of the point cloud, the coordinates of the point cloud can be processed to further determine the thickness of the kiln rings and refractory materials.

[0082] First, we filtered out the 3D point cloud data containing only the inner wall contours. We then processed the point cloud data using Open3D, NumPy for numerical calculations, Matplotlib for plotting, and the minimize function in SciPy for parameter optimization. We then converted the point cloud data into a NumPy array and sampled it, taking every 100 points to reduce the computational effort.

[0083] Secondly, we need to use the contour data conversion method to obtain the coordinates of the central axis of the point cloud in order to process the point cloud data into wall thickness information. This requires us to find a straight line so that the sum of the distances between the straight line and the given point cloud data is minimized. At this time, the straight line is the axis passing through the center of the rotary kiln. The minimization method can be used to optimize the parameters of the straight line. The SciPy minimize objective function is used to optimize the straight line parameters so that the sum of the distances from the point to the straight line is minimized. The BFGS method is used to optimize the minimum sum of the distances from the point to the straight line. In the implementation case, the objective function is the sum of the distances from all points in the point cloud to the straight line. The initial guess value and the point cloud data are passed as parameters for optimization. The specific process is as follows:

[0084] According to the geometric characteristics of the rotary kiln and engineering experience, the parameters of an initial line are set, including the direction vector of the line and the points it passes through. The parameters of the line can be expressed as the direction vector (dx, dy, dz) and a point P0 (x0, y0, z0) on the line.

[0085] The objective function is defined as the minimum sum of the distances from all points in the point cloud to the straight line; for any point P in the point cloud i (x i ,y i , z i ), its distance d to the straight line L _i It can be expressed as formula (2). The BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm is selected as the optimization algorithm. This algorithm is a quasi-Newton method suitable for solving unconstrained nonlinear optimization problems. Using SciPy's minimize function, the objective function and initial line parameters are used as input for iterative optimization. In each iteration, the BFGS algorithm updates the line parameters based on the gradient information, so that the objective function F gradually decreases.

[0086] Set the iteration termination condition, such as the decrease of the objective function is less than a certain threshold or the maximum number of iterations is reached. When the termination condition is met, the optimized straight line parameters are output.

[0087] According to the above optimized linear parameters, the central axis of the rotary kiln is determined;

[0088] Along the central axis, the rotary kiln is divided into several sections, each of which is a torus. Each torus is unfolded into a plane to obtain a series of annular plane diagrams;

[0089] When unfolding, the relative position relationship between the points on the torus needs to be maintained;

[0090] On the unfolded plane, the distance from each point to the central axis is measured, which is the wall thickness information of that point. The extracted wall thickness information is processed and analyzed using FARO SCENE software, such as drawing a wall thickness distribution map and calculating the average wall thickness, for easy engineering application and display.

[0091] The experimental results and analysis are as follows:

[0092] After iterating the above algorithm, the straight line through the central axis of the rotary kiln is finally found to be:

[0093]

[0094] Assume that the Cartesian coordinates are (x, y, z) and the cylindrical coordinates are (ρ, θ, φ), where ρ represents the polar radius (the distance from the origin), θ represents the polar angle (the horizontal angle with the x-axis), and φ represents the elevation angle (the angle with the z-axis).

[0095] Calculate the radial distance r: The radial distance is the perpendicular distance from a point to a specified axis. For each point (x, y, z), the projected distance to the axis can be calculated. Assume that the axis is from the starting point (x s， ys , z s ) and direction vector (dx, dy, dz), then the radial distance from any point in the point cloud to the central axis is for:

[0096]

[0097] Axial distance :

[0098]

[0099] Azimuth :

[0100]

[0101] Rotation angle :

[0102]

[0103] Finally, the azimuth and rotation angles need to be converted into degrees.

[0104] According to the outer diameter of the rotary kiln of 6.1m, the inner diameter r of the rotary kiln is converted into the wall thickness data d and visualized. The obtained rotary kiln wall thickness distribution cloud map is shown in the attached figure. Figure 6 As shown, the red area represents a thinner thickness, which means that the ring in this area is thinner or the resistant material is severely eroded, and the darker the color in the cloud map, the thicker the wall.

[0105] As can be seen from the figure, the wall thickness from the kiln head to the kiln tail shows a trend of first decreasing and then increasing. The wall thickness in the area 10-20m away from the kiln head is about 96mm, and the material erosion in this area is more serious.

[0106] In the section >20m from the kiln head, the wall thickness gradually increases until the overall wall thickness at the kiln tail reaches 300mm, indicating that the refractory material in this section is less corroded and some rings remain. After the kiln was stopped on site, the ring thickness in the section 0-15m from the kiln head was less than 50mm, and the thickness in most areas was close to 0. After the 15m section, the ring thickness gradually increased until it reached 110mm at 23.5m. After a brief drop, the ring thickness peaked at 200mm at approximately 30m. This indicates that in the middle and rear sections of the kiln, there were still many rings remaining after the kiln was stopped. The refractory material in the section 10-20m from the kiln head was severely corroded, with the refractory material at 15m from the kiln head at 85mm, which was the most severely corroded. The refractory material at the kiln head and kiln tail was less corroded, with an average thickness of approximately 150mm.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for measuring ring formation and refractory residual thickness in a rotary kiln, characterized by: The following steps are involved: Scan the outer wall and inner wall of the rotary kiln with a laser scanner to obtain relevant point cloud data of the rotary kiln; Pre-process the relevant point cloud data of the rotary kiln to obtain the point cloud data of the internal ring and refractory thickness of the rotary kiln; Using the point cloud data of the internal ring and refractory thickness of the rotary kiln, the contour data conversion method is used to calculate the coordinates of the central axis of the rotary kiln's full-area point cloud data, and then the wall thickness of the rotary kiln is obtained; Through the coordinate conversion measurement of point cloud data between polar coordinates and Cartesian coordinates, the wall thickness information of the rotary kiln is visualized, and a cloud map of the rotary kiln wall thickness distribution is obtained, thereby realizing the measurement of the rotary kiln ring and refractory residual thickness; The method of using the point cloud data of the internal ring and refractory thickness of the rotary kiln and using the contour data conversion method to calculate the central axis coordinates of the global point cloud data of the rotary kiln, and then obtaining the wall thickness of the rotary kiln includes the following steps: S31: Based on the pre-processed three-dimensional point cloud data of the inner wall contour of the rotary kiln, a straight line passing through the axis of the center of the rotary kiln is determined, and the parameters of the straight line passing through the axis of the center of the rotary kiln are optimized using a minimization method. An objective function is constructed: the sum of the distances from all points in the point cloud to the straight line is minimized. Then, an iterative algorithm method for solving unconstrained nonlinear optimization problems is used to optimize the objective function to obtain the coordinates of the central axis of the rotary kiln; S32: Calculating the radial distance between each point in the three-dimensional point cloud data of the inner wall contour of the rotary kiln and the central axis of the rotary kiln; S33: Based on the radial distance from each point in the three-dimensional point cloud data of the inner wall contour of the rotary kiln to the central axis of the rotary kiln, the radial distance is converted into wall thickness data according to the outer diameter of the rotary kiln.

2. A rotary kiln ring formation and refractory residual thickness measurement method according to claim 1, characterized in that: The model of the laser scanner used is FARO Focus3D S350P.

3. The method for measuring the ring formation and refractory residual thickness of a rotary kiln according to claim 1, characterized in that: In the process of scanning the outer wall and the inner wall of the rotary kiln by a laser scanner to obtain the relevant point cloud data of the rotary kiln, a target is used as a splicing mark. The registration of two adjacent stations should use no less than M same-name points for registration conversion. After registration, the residual mean square error of the same-name points should not be greater than the fixed proportion of the mean square error of the points required by the specified accuracy level; the adjacent point clouds are spliced ​​in a relative manner. After reaching a certain range, the automatic difference evaluation function is used to unify the various stations into the same coordinate system to realize the collection of relevant point cloud data of the rotary kiln.

4. The method for measuring the ring formation and refractory residual thickness of a rotary kiln according to claim 1, characterized in that: The process of preprocessing the rotary kiln related point cloud data is as follows: The 3D point cloud data containing only the inner wall contours was filtered out, and the point cloud data was processed using an open source library. The point cloud data was converted into an array and the array was sampled every N points.

5. The method for measuring the ring formation and refractory residual thickness of a rotary kiln according to claim 1, characterized in that: The process of converting the radial distance into wall thickness data according to the outer diameter of the rotary kiln is as follows: Along the central axis, the rotary kiln is divided into several sections, each of which is a circular surface; Each torus is unfolded into a plane to obtain a series of annular plane graphs; when unfolding, the relative position relationship between the points on the torus is maintained; On the unfolded plane, measure the distance from each point to the central axis, which is the wall thickness information of that point.

6. The method for measuring the ring formation and refractory residual thickness of a rotary kiln according to claim 1, characterized in that: The process of measuring the point cloud data coordinate conversion between polar coordinates and Cartesian coordinates is as follows: Calculate the radial distance from any point in the point cloud to the central axis according to the Cartesian coordinates (x, y, z) and the cylindrical coordinates (ρ, θ, φ) and convert the azimuth and rotation angles into degrees.

7. A device for measuring thickness of a rotating body, characterized in that: include: Acquisition module: Scan the outer and inner walls of the rotating body through a laser scanner to obtain relevant point cloud data of the rotating body; Preprocessing module: used to preprocess the point cloud data related to the rotating body to obtain the point cloud data inside the rotating body; Calculation module: used for point cloud data inside the rotating body, using the contour data conversion method to calculate the coordinates of the central axis of the full-domain point cloud data of the rotating body, and then obtain the wall thickness of the rotating body; The method of using the point cloud data of the internal ring and refractory thickness of the rotary kiln and using the contour data conversion method to calculate the central axis coordinates of the global point cloud data of the rotary kiln, and then obtaining the wall thickness of the rotary kiln includes the following steps: S31: Based on the pre-processed three-dimensional point cloud data of the inner wall contour of the rotary kiln, a straight line passing through the axis of the center of the rotary kiln is determined, and the parameters of the straight line passing through the axis of the center of the rotary kiln are optimized using a minimization method. An objective function is constructed: the sum of the distances from all points in the point cloud to the straight line is minimized. Then, an iterative algorithm method for solving unconstrained nonlinear optimization problems is used to optimize the objective function to obtain the coordinates of the central axis of the rotary kiln; S32: Calculating the radial distance between each point in the three-dimensional point cloud data of the inner wall contour of the rotary kiln and the central axis of the rotary kiln; S33: Based on the radial distance from each point in the three-dimensional point cloud data of the inner wall contour of the rotary kiln to the central axis of the rotary kiln, convert the radial distance into wall thickness data according to the outer diameter of the rotary kiln; Visualization module: It is used to visualize the wall thickness information of the rotating body through the coordinate conversion measurement of the point cloud data between polar coordinates and Cartesian coordinate systems, and obtain the wall thickness distribution cloud map of the rotating body.

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