• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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  • 优先权
    • 专利摘要:
    these are devices, methods and systems for detecting and measuring cracks in the lining of a compartment. a typical device includes a scanning device for acquiring a cloud of data points by measuring the distances from the scanning device to a plurality of points on the surface of coating material and a controller for fitting a polygonal mesh and a minimum surface through the cloud of data points, where a crack is detected by a portion of the polygonal mesh that contains a connected group of polygons that extend even beyond the minimum surface beyond a threshold distance.
    公开号:BR112017000959B1
    申请号:R112017000959-5
    申请日:2015-05-29
    公开日:2020-10-06
    发明作者:Thomas Lawrence Harvill
    申请人:Process Metrix, Llc.；
    IPC主号:
    专利说明:

    FIELD OF THE INVENTION
    [001] The realizations of the present invention refer, in general, to apparatus, methods and systems and, more particularly, to devices, processes, mechanisms and techniques for detecting and measuring cracks in metallurgical containers. BACKGROUND OF THE INVENTION
    [002] Metal containers or compartments of various sizes and shapes designed to retain molten metals are widely used in various industrial applications. Examples of such applications include, without limitation, gasification processes in energy and chemical production, electric arc furnaces (EAF), basic oxygen furnaces (BOF), pots, blast furnaces, degassers and argon-oxygen decarburization furnaces ( AOD) in steelmaking. As known in the art, these compartments are usually lined with refractory material installed in the form of brick or molded in monolithic blocks in order to protect the metallic part of the container from the high temperature contents placed in it; however, due to normal wear and tear of the refractory material through the combined effects of oxidation, corrosion and mechanical abrasion, some portion of the refractory surface in contact with the molten metal is lost during processing, so frequent inspection is required in order to ensure extended use by performing previously located repair, in order to avoid possible catastrophic failures and unnecessary or premature remodeling of the entire refractory lining of the container.
    [003] Figure 1 shows a conventional metallurgical compartment 2 that has a shell 4, an inner layer of refractory material 6 and an opening 8. The dashed line 7 in Figure 1 illustrates the original layer of refractory material before the compartment is placed in use. The difference between lines 7 and 6 is which existing systems are configured to detect, so that an operator decides when to take the service compartment out for repair. A specific wear pattern that creates potential scratches is cracks in the refractory material 6. The cracks allow the molten metal to flow close to the outer steel shell of container 4, thereby creating an increased likelihood of melting the shell 4. Melting the shell 4 is generally called “disruption” and is considered by some to be a mode of catastrophic failure that can cause significant damage and / or injury.
    [004] Initially, the characterization of the refractory thickness in these metallurgical compartments was performed visually by experienced operators. Given the hostile environment and the long downtime required, this approach was quickly abandoned with the advent of automated systems. As understood by those skilled in the art, conventional automated processes are known to measure the localized thickness, that is, the localized distance between the inner layer of refractory material 6 and the housing of compartment 4. A conventional method widely used to measure the coating thickness rest of metallurgical containers is laser scanning.
    [005] Figure 2 shows a conventional laser scan refractory lining thickness measurement system 10, as described for example, in US 2013/120738 and comprising a mobile cart 12, a laser scanning system 16 mounted to the same, and associated hardware and software located on the mobile cart 12. One of the goals of the laser scanning system 10, when used in metallurgical containers, is to accurately measure the coating thickness to allow a container to remain in service for a maximum of possible time and to indicate areas that require maintenance. A typical laser scanning system 14 includes a laser, a scanning device, optics, a photodetector, and receiver electronics (not shown).
    [006] Such lasers are configured to fire rapid pulses of laser light at a target surface, some at up to 500,000 pulses per second. A sensor on the instrument measures the amount of time it takes for each pulse to return from the target surface to the scanning device through a given field of view 16 in Figure 2 The light moves at a constant and well-known speed that the laser scanning system 14 can calculate the distance between it and the target with high precision. Repeating this in rapid succession, the instrument builds a complex ‘map’ of the measured surface. By calculating and / or comparing the changes between measured range maps of the internal surfaces of the refractory material 6 with reference measurement of the same surfaces, the changes are detected and evaluated for possible changes that could result in a failure of the enclosure 4. Single measurements can be made in 20 to 30 seconds. An entire map of the furnace interior, which consists of, for example, 4 to 6 measurements and more than 2,000,000 data points, can be completed in a short period of time (for example, less than 10 minutes). Laser scanning produces a large collection of data points, sometimes called a cloud of data points.
    [007] However, despite the progress briefly described above in characterizing the wear on the refractory material 6 of the metallurgical compartment 2, so far none of the devices, processes, and / or methods that exist are capable of detecting and measuring a crack in the refractory surface 6. For this reason, based at least on the inductions with conventional laser scanning devices noted above, to characterize the integrity of containers and to measure their surface profiles, it would be advantageous to have devices, methods and systems that have capacity to detect, measure, and / or characterize cracks in the refractory material 6. Such characterization would include the ability to quantify a maximum depth defend, location, orientation, length, average width, and maximum width. This information can then be presented to a knowledgeable user who would be able to determine the severity of a crack and assess whether the metallurgical vessel requires maintenance or new coating even before refractory sweeping results in refractory wear below minimum safety levels. DESCRIPTION OF THE INVENTION
    [008] One or more of the needs briefly described above or others known in the art are addressed by apparatus, methods, and processes for detecting and measuring cracks in the lining of a compartment. Such apparatus include a scanning device for generating a cloud of data points by measuring the distances from the scanning device to a plurality of points on the surface of the compartment lining material; and a controller connected to the scanning device, the controller being configured to fit a polygonal mesh through the cloud of data points and to fit a minimal surface through the cloud of data points, the gap being detected by a portion of the polygonal mesh containing a group of polygons that extend until after the minimum surface beyond a threshold distance and the gap is measured by calculating a plurality of dimensions of the group of polygons.
    [009] Methods for detecting and measuring cracks in the lining of a compartment are also within the scope of the present invention. Such methods include steps to fit, using a controller, a polygonal mesh through a cloud of data points, the polygonal mesh having a resolution specified by a user and in which the cloud of data points is collected by a scanning device connected to the controller by measuring distances from the scanning device to a plurality of points on the surface of the compartment lining material; and fit a minimal surface through the cloud of data points that the controller uses, the gap being detected by a portion of the polygonal mesh that contains a group of polygons that extend until after the minimum surface beyond a threshold distance and the gap is measured by calculating a plurality of dimensions of the polygon group. BRIEF DESCRIPTION OF THE DRAWINGS
    [010] The attached Figures (not drawn to scale), which are incorporated and constitute a part of the specification, illustrate one or more achievements and, together with the description, explain those achievements. In the Figures: Figure 1 illustrates a conventional metallurgical compartment that has a protective layer of refractory material; Figure 2 illustrates a conventional laser scanning system to characterize the refractory material within the compartment of Figure 1; Figure 3 illustrates an embodiment of a laser scanning system according to an embodiment of the present invention; Figure 4 illustrates a mesh representation of the compartment of Figure 1 covered with a cloud of data points acquired with the system of Figure 3 according to an embodiment of the present invention; Figure 5 shows an approximation of a portion of Figure 4; Figure 6 illustrates a two-dimensional sectional view of the mesh representation embedded in the data points of Figure 4 overlaid with a minimal surface embedded in the data points according to an embodiment of the present invention; Figure 7 illustrates a possible crack identified by a set of postulating facets of Figure 6 according to an embodiment of the present invention; Figure 8 illustrates another possible crack identified by a set of postulating facets of Figure 6; Figure 9 illustrates a three-dimensional representation of cracks detected in accordance with an embodiment of the present invention; Figure 10 illustrates a Table showing various crack parameters and dimensions identified in Figure 4 according to embodiments of the present invention; Figure 11 illustrates a flow chart of a method according to an embodiment of the present invention; and Figure 12 illustrates a computer system configured to identify and characterize the cracks in the compartment of Figure 1 according to an embodiment of the present invention. DESCRIPTION OF ACCOMPLISHMENTS OF THE INVENTION
    [011] The following description of the achievements refers to the attached Figures. The same reference numbers in different Figures identify the same or similar elements. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. The following achievements are discussed, for simplicity, with respect to the terminology and structure of apparatus, systems, or methods for detecting and measuring cracks in refractory linings used to protect containers or compartments used in the metallurgical industry. However, the achievements to be discussed below are not limited to these sets, however, they can be applied to other devices, systems or methods, which include, however, are not limited to characterize, detect, profile and / or measure cracks in the coating of other compartments configured to retain or transport substances that have a temperature above the melting point of the materials from which the compartment is made.
    [012] References throughout the specification to "an embodiment" mean that a particular feature, structure or feature described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, the occurrence of the expression “in a realization” in several places throughout the specification does not necessarily refer to the same realization. Furthermore, particular resources, structures or characteristics can be combined in any suitable way into one or more realizations.
    [013] The present invention describes devices, systems and processes that analyze a cloud of data points obtained by weighting the refractory lining of a metallurgical container in order to identify cracks in it. Then, the algorithms used identify and quantify each gap in terms of the maximum gap depth, location, orientation, length, average width, and maximum width. A person skilled in the art can use this information to determine the severity of a crack and to assess whether the metallurgical container requires maintenance or new coating.
    [014] Figure 3 illustrates an embodiment of a laser scanning system 20 that is capable of generating a cloud of data points according to an embodiment of the present invention. Generally speaking, this laser scanning system 20 comprises two main components: a scanning device 22 and a general controller 24. The laser scanning system 20 and general controller 24 can be arranged together in the same device or be separated one from the other. For example, a mobile cart realization can include both in the same unit. In another embodiment, the laser scanning device system 20 can be a unit properly configured to be positioned in front of the compartment to be characterized and the general controller 24 to be positioned in another location (for example, in the operation control room of the power plant). As used in the present context, the general controller 24 can also be called a data reduction device 24 and / or a computing or computer device 24.
    [015] In operation, through field of view 16, the scanning device 22 scans the refractory material 6 in compartment 2, which generates a cloud of data points to be transferred to the general controller 24. According to one embodiment, scanning data from the scanning system 20 is processed as discussed now. It appears that in the field of characterizing the wear of metallurgical compartments, there are existing processes to collect data submitted to scanning and to apply several known processing steps in order to generate an image of the coating surface in order to identify areas in need of repair. Based on this image, which can be represented in print, on canvas, tabular, etc., a metallurgical compartment specialist determines whether repair of the compartment lining is necessary and consequently advises a metallurgical company. The achievements discussed below improve this technological process of determining the merit of a metallurgical compartment, for example, by detecting and characterizing cracks in the coating material in order to improve safety and to extend the compartment life.
    [016] In conventional systems this cloud of data points has been used, until now, to characterize the wear in the refractory material 6. As the technicians in the subject will verify, except those observed and identified throughout this document, none of the resources limitations of the present invention are considered in the laser scanning system 20 and general controller 24. In one embodiment, the laser scanning system 20 includes a laser, a scanning device, optics, a photodetector and receiver electronics. Many different types of lasers, scanning devices, optics, photodetectors and receiver electronics that exist are capable of collecting a cloud of data points that characterize the surface of the refractory material 6. In one embodiment, the laser scanning system 20 is a specific implementation of a more general classification of measurement systems known as LiDAR (light detection and telemetry system or laser telemetry, detection and imaging). In such realizations, any type of LiDAR system has the capacity to produce the appropriate data point cloud for crack detection analysis if the accuracy of the device is at least half of the resource sizes to be detected. Once acquired, the cloud of data points is transferred to the general controller 24 for further analysis, as will be further explained below. In one embodiment, the laser scanning system 20 comprises an Anteris laser scanning device, which has a small beam diameter (about 4 mm), a high precision scan (error in the range of ± 3 mm), large rates (up to 500,000 Hz), a robust design suitable for the factory environment and the thermal loads imposed when scanning high-temperature surfaces, laser wavelength safe for the eyes (which eliminates and / or substantially reduces the worries of workplace safety), vertical scan angle of ± 40 °, and horizontal scan angle of 0 to 360 °. Such laser scanning devices support standard resolution scans of a container interior in about 6 to 10 seconds, which results in less container downtime and greater production availability. In high resolution mode, the Anteris scanner can provide detailed images of the container that can be used to detect cracks, define the region around a leak orifice, or a condition of a cleaning plug.
    [017] Desirable features of the laser scanner device system 20 include time accuracy to provide desired levels of range accuracy, angular measurement accuracy and beam sizes that will provide the desired total accuracy as noted above. The minimum detectable resource size is dependent on the scanning device's ability to spatially resolve an individual scanned point. The scanning device uncertainty can be considered as a sphere around a point with an oscanner radius. The use of σscanner as the first standard deviation of the measurement uncertainty means that there is an 86% probability that the measured point is within the sphere of uncertainty. The use of these metric and heuristic data, the minimum viable resource size, that is, what can be seen, is twice the scanning device uncertainty. This statement is subjected to a measurement resolution that is less than or equal to the scanning device uncertainty. The measurement resolution is the spatial separation of points on a measured surface. Scanning or measuring device uncertainty is dominated by at least three terms, that is, range uncertainty (OR), angular measurement uncertainty (OAngie) and beam diameter uncertainty (OB). By assuming that these are random variables, a person can estimate the scanning device uncertainty as the sum of the squares of reach and angular uncertainties. The range uncertainty is dependent on the scanning device's ability to measure the range, therefore, a timing uncertainty (or δt). Angular uncertainty is dependent per point on target reach (R), such as:

    [018] and the scanning device uncertainty, oscanner, is then calculated by:

    [019] where, the beam uncertainty, OB, is equal to half the beam diameter. With the quantities verified above, the minimum detectable crack size, or Çcrack.Min, is equal to twice the scanning device uncertainty. In a practical system that uses a laser, the scanning device uncertainty will sometimes be limited by the beam size.
    [020] Generally speaking, once the cloud of data points is generated, cracks are detected and measured by initially fitting the cloud with a high-resolution polygonal mesh surface, SHR, the said surface having , in some embodiments, a resolution defined or chosen by the user. As used throughout this document, the term "high resolution" means spacing of average measuring point on the surface in question of less than or equal to about 5 mm which leads to a minimum detectable resource size of about 10 mm.
    [021] Subsequently, a minimum surface area, Smin, is calculated for the data point cloud. The comparison of SHR and Smin will allow the identification of data points that are located at a greater distance than a specified distance from the Smin, thereby identifying all points of the SHR that possibly belong to the cracks. As will be explained further below, in one embodiment, such a comparison of SHR and Smin is performed by identifying all facets of the SHR that have vertices that are outside the Smin, that is, the facets that are greater than a programmable distance from the SHR, therefore, they generate a set of potential facets that belong to the slits in the refractory material 6. Finally, through additional treatment of the vertices that are outside the SHR, connected facets are grouped into single slits and the slit characterization in terms of orientation of slot, slot length, maximum slot depth location, average slot width and maximum slot width is played. Each of these portions of the present invention will now be covered in more detail considering the various embodiments.
    [022] Figures 4 and 5 illustrate a high resolution mesh representation of the metallurgical compartment generated from and covered with the data point cloud acquired from the compartment with the scanning system of Figure 3 according to one embodiment of the present invention. Figure 4 is an illustration of the entire compartment and Figure 5 illustrates an approximate portion of Figure 4 In the example given in Figures 4 and 5, a triangular mesh was used. However, those skilled in the art will verify that other geometries could be used to generate such a mesh. In addition, the resolution of such a mesh must be defined or selected by the user, as already verified; with the understanding that thinner meshes will take more computational time to create and analyze than thicker meshes. In addition, the resolution can be selected according to the resolution of the data point cloud - a higher density of data points that lead to the ability to choose a finer mesh resolution. For this reason, the mesh size should be considered as an arbitrary variable and not one that could limit the subject of the present invention.
    [023] Additionally, in some embodiments, high resolution scans are first obtained and the crack characterization and detention procedures described in this document are then conducted. In other embodiments, low-resolution scans are primarily used to identify areas where possible cracks are located. Subsequently, high resolution scans are then performed only from the area where possible cracks are located.
    [024] Generally speaking, the mesh size is greater than the precision of the light source that is used to scan the compartment. For example, for a laser that has an accuracy of ± 5 mm, the resolution mesh size to be used can be chosen to be 25 mm. As seen, thinner meshes can delay the data reduction process. For example, the data collected in Figure 4 has about 1 M + data points per measurement. When converted to a data structure, using a triangular mesh in this example, all facets (that is, each small triangle generated by fitting the data) must be created. For this reason, the processing speed increases with the use of fewer triangles. For example, in a triangle mesh with 1 M data points, for any type of mathematical operation (for example, creating a cross section of the mesh, calculating different desired volumes or measuring different desired distances for various points), the time scales computationally increase geometrically with the size of the structures. Thus, as known to those skilled in the art, mesh size is an important computational constraint to be considered. Users expect results close to real time, in order to be able to manipulate and look at the calculated data, therefore, a balance between mesh size and computational time is always present.
    [025] Another consideration when generating the mesh is noise. The cloud of data points generated by the scanning system is noisy in nature and can also include acquired data that are statistical exceptions, therefore, that have to be removed since they do not belong to the surface that is characterized. Different processes can be used to reduce noise in the scanned data. For example, a least square fit can be used to reduce or filter out noise. In addition, crack detection and measurement are sensitive to the resolution selected by the user. For example, a gap in the order of 25 mm can be reliably detected using a laser with an accuracy of ± 5 mm. If a selected grid size is equal to the gap size that a person wants to measure, it should be expected that such a gap can be detected, but not quantified.
    [026] The gray surface illustrated in Figure 4 is a surface that results from a better fit of all data using a least squares fit, that is, SHR, therefore, which essentially results in an approximation of least squares of the gross cloud of data points. Limited by the resolution used to collect the raw cloud of data points, in order to better or more precisely fit the data to the slit features to be identified and characterized, the mesh mesh is refined to smaller polygonal elements (for example, triangular elements) . The black dots shown in Figure 5 are actual data points overlaid on the shown embedded surface. Therefore, in some embodiments, the generated surface is a relatively small subsection of that of the totality. Throughout this description, the surface illustrated in Figures 4 and 5 will be called a high resolution mesh surface, or SHR.
    [027] Since the high resolution mesh surface, SHR, is generated with a resolution defined by a user, the minimum surface, Smin, is calculated for the cloud of data points. The systemic comparison of SHR and Smin will then allow the identification of data points that are located at a greater distance than a specified distance from the minimum surface, thereby identifying all points of the SHR that are possibly associated with cracks. Figures 6 to 8 illustrate several completely or partially two-dimensional sections of the data in Figure 4, which shows data points and both high-resolution and minimal surfaces.
    [028] The information generally illustrated in Figure 6 is as follows. The steel casing that lines the compartment is shown at 30. A permanent liner 32 is disposed next to casing 30, which further provides permanent protection. Next to the permanent liner 32 is the refractory layer 6, sometimes referred to as the active liner 34. The cracks in the active liner 34 are the defects that a person is normally concerned with. Holes 36 are emptying holes used to dispose of materials a from the compartment and / or other holes used to agitate or clean the compartment during material processing. In some scanning methods, these features can be used as references to precisely position the compartment before measuring the active coating 34.
    [029] In one embodiment, SHR is a least squared best fit and Smin is the first negative standard deviation, p, of SHR, and this surface is hereinafter referred to in this document SM, min. The high resolution surface (SHR), the minimum surface (Smin), and the data points are shown in 38 next to the outer surface of the active coating 34. In another embodiment, Smin is a minimal surface constructed by subtracting from SHR the local negative standard deviation of SHR. The least squared surface better fit SHR can be considered as the surface created by placing a blowing balloon inside the compartment, however, not very firm in order to fit all the cracks to be located and measured. In the enlarged views shown in Figures 7 and 8, in 40, SHR, SM, min and the actual data diverge, in which the best fit shows a depression in the minimum surface located at the top of it, therefore, identifying possible slots.
    [030] As understood by the technicians in the subject, there are different ways to remove noise and / or statistical exceptions from the acquired data and to generate SHR. For example, the generation of such a high-resolution mesh surface, SHR, can be performed by techniques, such as marking cubes, CRUST and / or Poisson, to name just a few. As already indicated, a desired feature of such algorithms to enable viable crack detection is that the plug-in algorithm is immune to noise. Noise immunity is a desired feature since the entry point cloud is typically noisy for several reasons, which include, but are not limited to, the uncertainty of scanning device and / or data points falsely acquired from smoke, dust and / or residues, which are also examples of statistical exception data points. An advantageous fitting technique would employ a variable mesh size contingent at the level of detail of the data point cloud. Those skilled in the art will understand that resolving such fitting techniques will affect the minimum size of the cracks to be detected and measured. For example, a resolution of 25 mm will result in the accuracy of the slit calculation algorithm to be limited to approximately half of that value, that is, 12 mm. Additionally, the generation of the SHR surface can also be performed by processing the acquired data in smaller sets, in order to improve the speed while maintaining an acceptable data set size.
    [031] In realizations using SM, min, such a statistical representation of the embedded surface can be calculated so that for each facet, Fi, on the embedded surface SHR, and for each point in the data point entry cloud, Pj, by normal distance, Dj, from Pj to Fi is first calculated followed by determining the average normal distance DI.AVG and a standard deviation, σi, of all calculated normal distances. Subsequently, for each facet, Fi, on the embedded surface SHR and for each point in the cloud of entry point, Pj, SM, min can be calculated as follows:
    [032] If (Dj - DI, AVG) <0, Dj is added to DmTOTAL and a numerator, NM is added; (3)
    where it is the scalar product between the variables shown in which ΠFI θ is a vector of unit normal to facet Fi. Based on Equations (3) to (5), Sp.rnin is then constructed from the Pmi points.
    [033] Slots can be identified and measured by comparing SHR and SM, min once a slot size selected by operator is specified. Those skilled in the art will verify that a programmable distance is necessary in order to control the number of facets that will fit a condition designed to find cracks in order to characterize them. The cracks will only become significant when they are a given size. All facets that satisfy the slot size selected by the operator are identified as possibly belonging to a slot. All such facets are adjusted separately and determined later if they belong to the same group, that is, the same gap.
    [034] The cracks are initially filtered by extracting all facets, Fi, from the high-resolution SHR mesh that has any vertex outside the SM, min by a distance that is greater than a programmable distance, Ωm. These are all combined into a slit or scc postulant surface. In order to identify all facets that belong to a single crack, for all facets in Scc, those with common vertices are connected on a defensive surface, Sc, thereby creating a group SRC.Ide I subsurface (SRC.I is a subsurface of Sc, which is a subsurface of Scc).
    [035] Mathematically, Sc contains groups of connected facets of the SHR that are slit surfaces, so for each vertex, Vj, on each facet, Fi, on the embedded surface SHR, first the celebrated Euclidean distance, Dmj, from the Vj to S | j, min is calculated. Subsequently, if Dmj> Ωm then Fi is added to the surface of postulating cracks, Scc, where Ωm is the user-selected programmable parameter. Subsequently, a person recurrently groups the facets in Scc with those facets that have any common vertices, thus forming groups on the crevice surface SRC.I- Groups in SRC.I are recurrently grouped together to form Sc by combining the surfaces in SRC.I if a minimum distance between surfaces, DRC and another programmable parameter, Ωc, is satisfied. Ωc can be considered as a physical distance, so that if one has two slits that are “close” (inside Ωc) and that point in the same direction, then they can be considered the same slit, so it is created the slit surface collection, Sc.
    [036] Statistics for each slot are then calculated with the information in Scc. That is, in one embodiment, the Euclidean distance from each vertex in Sc to SM, min can determine the mean gap depth, DCAVG. The maximum slot depth, DCMAX, and slot location can be determined in another embodiment by the maximum Euclidean distance from each vertex in Sc to SM, min. Finally, fitting a line of least squared, best fit across all vertices in a slot can be used to determine the slot orientation, which would correspond to the direction of the best fit line.
    [037] Another way to quantify the detected and measured cracks is to determine their orientation. Orientation is a desirable feature due to the way that certain containers are constructed. Depending on the construction characteristics of a given container, cracks most likely occur along the brick lines. Upon learning from the main orientation that, for example, the refractory material may have been disposed, a person can search for and characterize the cracks that are substantially aligned with that main orientation depending on a particular application. Those skilled in the art will verify that the devices, systems, methods and processes that are revealed are general. In this way, a person has the capacity to search for a certain direction or facets together grouped along that direction. In addition, the gains experienced in working with certain types of containers and their refractory materials, better programmable orientation can be decided by experience, type of application, how the bricks were extended, the orientation selected as a function of brick type, and / or expected type of crack in a given application, to name just a few examples.
    [038] For example, if vertical cracks are somehow prevalent in a given application, cracks within, for example, ± 30 ° of a vertical geometric axis (for example, a Z geometric axis) that have a relationship medium width by length, or RL / W, greater than a programmable minimum threshold value, or RL / W, MIN, can be searched in the reduced data. Similarly, if horizontal cracks are somehow prevalent in another application, the cracks within, for example, ± 30 ° of a horizontal plane (for example, an XY plane) that has an average width to length ratio , RL / W, greater than a programmable minimum threshold value, or RL / W.MIN, can then be identified in the reduced data.
    [039] For each SRC.Iem SRC, one determines the length, orientation, maximum depth, average width, maximum width and location by first connecting to other SRC.J subsurface to create a new consolidated subset Sc, i. Must have the same orientation as SRC.J. SRC.It must be within a maximum distance Ωc, of SRC.J. and, finally, SRC.J should have a greater average width-by-length ratio than SRC.Ie SRC.J, that is, RL / W.J> RL / W, I. For each facet in Sc, one person calculates the gap depth. The gap depth is defined as the maximum Euclidean distance between each vertex in Sc.i and SM, min. To improve the precision depth calculation algorithms within the scope of the present invention, it is possible to optionally refit SHR only in the region defined by Sc.i to create an embedded surface that has a higher resolution than the first employed.
    [040] Technicians in the field will verify that SRC.Is the subgroup that met the filtering criteria data, however, they may not be connected to the other subgroups directly - in fact, they touch each other. Thus, a person can detect a crack or possibly a lump of processing materials that can have a crack loaded in a small portion of it. The last processing steps just described are, therefore, an evaluation of a proximity criterion that could be adjusted to group the subgroups into super subgroups. If they are close enough and follow approximately the same orientation, they are the same gap. In this way, Ωc admits filling materials, and, after the first grouping, the proposed processes will now check again based on all cracks that have met all conditions. In the given explanation, i is for all groups that met the criteria data and j is for everyone. Those skilled in the art will find that i cannot be equal to j because, if so, the given condition would be satisfied every time - clearly an undesirable outcome.
    [041] As the technicians in the subject will verify, an average width to length ratio is a desirable variable to be considered and one that should be chosen depending on the type of application being examined and the characteristics of which types of cracks are seen. Once a value for this variable is specified and the data filtered, all possible applicants who fit the adjusted criteria will be taken and the user can, for example, fit a line of best fit through all those facets - by putting a bounding box around the selected facets is effective. For this adjustment, if a group of facets has a width-to-length ratio of about one, for example, it is a crater and not a crack. A crack will normally be characterized by a long longitudinal dimension in relation to a transverse dimension. In this way, having the ability to specify an average width to length ratio, the products, processes and systems that are revealed will have built-in flexibility. Typically a ratio of about 4 can be specified, however, it will depend on the type of order and other variables known to those skilled in the art. For example, crevices in pans may have an average width-to-length ratio that is probably greater than 4. In another order, users may want to look for very large crevices, sometimes even craters, like a position where a brick has fallen - a great totality. Thus, one of the advantageous features of the products, processes and systems that are revealed is the flexibility to adjust an average width-to-length ratio as a function of what is being viewed or the order in question.
    [042] The darkest regions identified as 50 in Figure 9 illustrate cracks in the Figure 4 compartment. These cracks were detected and characterized as a result of the procedures and / or calculations discussed above conducted in the cloud of data points illustrated in the same Figure. The Table in Figure 10 illustrates values, for each gap detected, of slot orientation, maximum depth, location in cylindrical coordinates (radius (R), angle (0), and longitudinal distance (Z)), slot length, average width and maximum width.
    [043] Methods and processes configured to detect / identify, measure and characterize cracks in the lining of a container or compartment are also within the scope of the present invention. Figure 11 illustrates the flow chart of an embodiment of a method or process 100 according to the present invention. As shown, in 110, such methods include fitting a polygonal mesh through a cloud of data points, with the polygonal mesh having a resolution specified by a user, in which the cloud of data points is collected by a scanning device. measuring the distances from the scanner to a plurality of points on the surface of the compartment lining material. At 120, fit the minimum surface through the cloud of data points using the controller. And, in 130, identify / detect a crack by a portion of the polygonal mesh that contains a group of polygons that extend until after the minimum surface beyond a threshold distance selected by the user and measure a plurality of dimensions of the group of polygons.
    [044] One or more of the steps of the methods comprising the present invention can be implanted in a computer system configured specifically to detect / identify, measure and characterize cracks in the refractory lining of a metallurgical container or compartment as explained above in this document . An example of a representative computing system that is capable of conducting operations according to the achievements is illustrated in Figure 12 Hardware, firmware, software or a combination of them can be used to perform the various steps and operations described in this document.
    [045] The computing system 900 suitable for carrying out the activities described in the achievements may include a server 901. Such a server 901 may include a central processor (CPU) 902 coupled to a random access memory (RAM) 904 and a memory only read (ROM) 906. ROM 906 can also be other types of storage media for storing programs, such as programmable ROM (PROM), erasable PROM (EPROM), etc. The processor 902 can communicate with other internal and external components through sets of input / output (I / O) circuits 908 and bus 910, to provide control signals and the like. CPU 902 conducts a variety of functions as is known in the art, as determined by software and / or firmware instructions.
    [046] Server 901 may also include one or more data storage devices, which include disk drive 912, CD-ROM drives 914 and other hardware that can read and / or store information such as a DVD, etc. In one embodiment, the software to carry out the steps discussed above can be stored and distributed on a 916 CD-ROM, a 918 removable memory device or other form of media that is capable of storing information in a portable manner. Such storage media can be inserted and read by devices, such as CD-ROM drive 914, disk drive 912, etc. The server 901 can be attached to a display 920, which can be any type of display or known presentation screen, such as LCD displays, plasma displays, cathode ray tubes (CRT), etc. A 922 user input interface is provided, which includes one or more user interface mechanisms, such as a mouse, keyboard, microphone, touch pad, touch screen, voice recognition system, etc.
    [047] The 901 server can be coupled to other computing devices, such as fixed and / or wireless telephone terminals over a network. The server can be part of a larger network configuration such as a global area network (GAN), such as the Internet 928, which allows permanent connection to the various fixed and / or mobile client devices.
    [048] The disclosed achievements provide apparatus, methods and systems for detecting / identifying, measuring and characterizing cracks in the coating of a metallurgical container or compartment as well as the other uses described briefly above in this document and verified by those skilled in the art after consideration of this invention. It should be understood that this description is not intended to limit the invention. On the contrary, the realizations are intended to cover alternatives, modifications and equivalents, which are included in the scope of the invention as defined by the appended claims. In addition, in the detailed description of the embodiments, several specific details are presented in order to provide a comprehensive understanding of the claimed invention. However, a person skilled in the art would understand that various achievements can be practiced without such specific details.
    [049] Although the resources and elements of the present achievements are described in the achievements in particular combinations, each resource or element can be used alone without the other resources and elements of the achievements or in various combinations with or without other resources and elements revealed in the present invention.
    [050] This written description uses examples of the present invention to allow any person skilled in the art to put them into practice, including producing and using any devices or systems and performing the built-in methods. The patentable scope of the present invention is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims.
    [051] Although the present embodiments described have been shown in the Figures and completely described above, with particularity and in detail, in conjunction with various achievements, it will be apparent to those skilled in the art that many modifications, changes and omissions are possible without there being materially the deviation from the scope of the innovative teachings, the principles and concepts presented in this document, and the advantages of the present invention mentioned in the attached claims. Consequently, the appropriate scope of the innovations revealed should be determined only for the broader interpretative purposes of the appended claims, so as to cover all such modifications, changes and omissions. In addition, the order or sequence of any process steps or method can be varied or rescheduled, according to alternative achievements. Finally, in the claims, any clause of the means-plus-function type is intended to encompass the structures described in this document as they perform the mentioned function and not only structural equivalents, but also equivalent structures.
    权利要求:
    Claims (20)
    [0001]
    1. APPLIANCE CONFIGURED TO DETECT AND MEASURE A SLIT (50) on a surface of a coating (7) of a compartment (2), the apparatus comprising: a scanning device (20) that has a laser, optics, a scanning device (22), a photodetector, and receiver electronics, the scanning device (22) is configured to generate a cloud of data points (SHR) by measuring distances from the scanning device (22 ) to a plurality of points on the housing lining surface (2); and a controller (24) connected to the scanning device (20), characterized by the controller (24) being configured to fit a polygonal mesh through the data point cloud (SHR) using a resolution selected by a user, and to fit a minimal surface (Smin) through the cloud of data points, where the controller (24) is further configured to: - detect the gap (50) by a portion of the polygonal mesh that contains a group of polygons that extend until afterwards of the minimum surface beyond a threshold distance selected by the user; and - measuring the gap (50) by calculating a plurality of dimensions of the polygon group.
    [0002]
    2. APPLIANCE, according to claim 1, characterized in that the controller is additionally configured to remove statistical exceptions from the data point cloud (SHR) before fitting the polygonal mesh and the minimum surface (Smin).
    [0003]
    3. APPLIANCE, according to claim 1, characterized by the controller being configured to obtain the polygonal mesh by a minimum square, better fit of the data point cloud (SHR) and to determine the minimum surface (Smin) as a first deviation least square negative pattern, better fit.
    [0004]
    4. APPARATUS, according to claim 1, characterized by the controller being configured to calculate the minimum surface (Smin) by calculating a normal distance from each facet of the polygonal mesh to each point in the data point cloud (SHR) and determining an average normal distance and standard deviation from the calculated normal distances, in which, for each facet on the embedded polygonal surface and for each point in the entry point cloud, the controller is configured to calculate the minimum surface (Smin) by products scalars between normal unit vectors for corresponding facets and the calculated normal mean.
    [0005]
    Apparatus according to claim 1, characterized in that the threshold distance is a function of a slot size (50).
    [0006]
    6. APPLIANCE, according to claim 1, characterized in that the controller is configured to determine an average gap size (50) by weighting the distances from each facet of each polygon in the connected group of polygons and to calculate a maximum depth slit (50) determining a maximum distance from the distances of each vertex of the polygon in the connected group of polygons to the minimum surface.
    [0007]
    Apparatus according to claim 1, characterized in that the resolution of the scanner (22) is equal to or less than half of a characteristic dimension of the slot (50) that is measured.
    [0008]
    APPARATUS according to claim 1, characterized in that the plurality of dimensions includes an orientation of the slot (50) in relation to the compartment (2).
    [0009]
    Apparatus according to claim 8, characterized in that an angular band for the orientation is specified and only slits (50) that have an average width to length ratio greater than a minimum threshold value are identified.
    [0010]
    10. METHOD FOR DETECTING AND MEASURING A GAP (50) on a surface of a lining of a compartment (2), the method being characterized by understanding the steps of: fitting a polygonal mesh through a cloud of data points ( SHR), with the polygonal mesh having a resolution specified by a user and in which the cloud of data points (SHR) is collected by a scanning device (22) by measuring the distances from the scanning device (22 ) to a plurality of points on the surface of the casing material (2); and fit a minimal surface through the cloud of data points (SHR), where the gap (50) is detected by a portion of the polygonal mesh that contains a group of polygons that extend until after the minimum surface (Smin) in addition to a threshold distance selected by the user and the gap (50) is measured by calculating a plurality of dimensions of the polygon group.
    [0011]
    11. METHOD, according to claim 10, characterized in that it further comprises the step of: filtering the statistical exceptions out of the data point cloud (SHR) before fitting the polygonal surface and fitting the minimum surface (Smin).
    [0012]
    12. METHOD, according to claim 10, characterized by the fitting of the polygonal mesh additionally comprising the step of: fitting a minimum square, better fitting of the data point cloud (SHR) and the fitting of the minimum surface (Smin) comprises a calculation of a first negative standard deviation of the least square, best fit of the data point cloud (SHR).
    [0013]
    13. METHOD, according to claim 10, characterized by the fit of the minimum surface (Smin) additionally comprising the steps of: calculating a normal distance from each facet of the polygonal mesh to each point in the cloud of data points (SHR) ; and determine an average normal distance and a standard deviation from the calculated normal distances, in which, for each facet on the embedded polygonal surface and for each point in the cloud of entry point, the minimum surface (Smin) is calculated by scalar products between vectors of normal units for corresponding facets and the normal average calculated.
    [0014]
    14. METHOD, according to claim 10, characterized by additionally comprising the steps of: calculating an average gap size (50) by weighting the distances from each facet of the polygon in the connected group of polygons to the minimum surface ( Smin); and calculate a maximum gap depth (50) by determining a maximum distance from the distances of each vertex of the polygon in the connected group of polygons to the minimum surface (Smin).
    [0015]
    15. METHOD, according to claim 10, characterized in that the polygonal mesh is a triangular mesh.
    [0016]
    16. METHOD according to claim 10, characterized in that the resolution of the scanner (22) is equal to or less than half of a characteristic dimension of the slit (50) that is measured.
    [0017]
    17. METHOD, according to claim 10, characterized in that it further comprises the step of: separating the slits (50) in the group of polygons by groups of polygons that have common vertices or common directions.
    [0018]
    18. METHOD according to claim 10, characterized in that the plurality of dimensions includes an orientation of the slot (50) in relation to the compartment (2).
    [0019]
    19. METHOD according to claim 18, characterized in that an angular range for the orientation is specified and only slits (50) that have an average width-to-length ratio greater than an average width-to-minimum ratio length are identified.
    [0020]
    20. METHOD, according to claim 19, characterized by the ratio of average width to length and the minimum threshold value being a function of an industrial application of the compartment (2).
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    EA031423B1|2018-12-28|
    EP3169990A1|2017-05-24|
    TR201909444T4|2019-07-22|
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    EA201790036A1|2017-05-31|
    PL3169990T3|2019-10-31|
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    EP3169990A4|2018-03-21|
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    JP6470394B2|2019-02-13|
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    法律状态:
    2020-02-04| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-06-23| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2020-10-06| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 29/05/2015, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US201462026052P| true| 2014-07-18|2014-07-18|
    US62/026,052|2014-07-18|
    PCT/US2015/033200|WO2016010635A1|2014-07-18|2015-05-29|Crack detection and measurement in metallugical vessels|
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