Corrosion evaluation method, recording medium, program, and corrosion evaluation device

Abstract

A corrosion evaluation method according to the present invention comprises a step (S1) for acquiring a cross-sectional image of a pipe and a step (S8) for evaluating the cross-sectional image so as to obtain radial corrosion information that indicates the degree of corrosion in the radial direction of the pipe in the cross-sectional image. More preferably, the corrosion evaluation method comprises steps (S2, S3, S4) for obtaining the center coordinates of the minimum circumscribed circle for a pipe portion in the cross-sectional image of the pipe and performing a polar coordinate transformation on the cross-sectional image of the pipe with reference to the center coordinates. According to this corrosion evaluation method, the degree of corrosion in the radial direction, which is the depth of corrosion in a metal pipe, can also be taken into consideration in the evaluation.

Description

Corrosion Evaluation Method, Recording Medium, Program, and Corrosion Evaluation Apparatus

[0001] The present invention relates to a method for evaluating corrosion of a pipe, a recording medium, a program, and a corrosion evaluation apparatus.

[0002] Japanese Patent No. 6887535 (Patent Document 1) discloses a technique of taking a radiographic image of a metal pipe and analyzing it. This technique has been proposed for use in evaluating the area of a corrosion damage location in the cross-section of a metal pipe.

[0003] Japanese Patent No. 6887535

[0004] When evaluating a corrosion damage location by the method described in Japanese Patent No. 6887535 (Patent Document 1), although the degree of corrosion in the circumferential direction, that is, the area of the corrosion damage location in the pipe cross-section, can be considered in the evaluation, there is a problem that the degree of corrosion in the radial direction, that is, the depth of corrosion, cannot be evaluated.

[0005] In view of the above situation, the present disclosure aims to provide a corrosion evaluation method that can also consider the degree of corrosion in the radial direction, that is, the depth of corrosion, in the evaluation.

[0006] The present disclosure relates to a method for evaluating corrosion of a pipe. The corrosion evaluation method includes a step of acquiring a cross-sectional image of the pipe and a step of evaluating the cross-sectional image to obtain radial corrosion information indicating the degree of corrosion in the radial direction of the pipe in the cross-sectional image.

[0007] According to the corrosion evaluation method of the present disclosure, it is possible to provide a corrosion evaluation method that can also consider the degree of corrosion in the radial direction, that is, the depth of corrosion, in the evaluation.

[0008] This is a block diagram showing the schematic configuration of the corrosion evaluation device in Embodiment 1. This is a functional block diagram of the information processing unit in Embodiment 1. This is a schematic diagram showing the dimensions of the piping, the imaging interval of the cross-sectional images, and the imaging range in the longitudinal direction in Embodiment 1. This is a flowchart showing the method for analyzing cross-sectional images in Embodiment 1. This is an example of a cross-sectional image of a pipe with an internally corroded surface. This is an example of a cross-sectional image after polar coordinate transformation. This is an example of the result of obtaining the coordinates of the contour portion of the pipe cross-section. This is a diagram explaining the corrosion evaluation method in Embodiment 1. This is an example of numerical data representing the corrosion depth at each deviation angle in the circumferential direction. This is an example of a cross-sectional image in which the corroded area has been colored after polar coordinate transformation and binarization processing. This is an example of a pipe cross-sectional image in which the corroded area has been colored. This is a flowchart showing the method for counting the corroded areas and the method for evaluating the maximum corrosion depth of each corroded area. This is an example of numerical data representing the corrosion depth in all cross-sectional images. This is an example of an image visualizing the depth and location of corrosion holes. This is an example of a histogram of the maximum corrosion depth at each corrosion hole. This is a schematic diagram showing the dimensions of the piping and the imaging interval of the cross-sectional images in Embodiment 2. This is a flowchart showing the method for analyzing cross-sectional images in Embodiment 2. This is an example of a cross-sectional image of a pipe with external corrosion. This is an example of a cross-sectional image after polar coordinate transformation. This is an example of the result of obtaining the coordinates of the outline of the pipe cross-section. This is a diagram illustrating the corrosion evaluation method in Embodiment 2. This is an example of a cross-sectional image in which the corroded areas have been colored after polar coordinate transformation and binarization. This is an example of a pipe cross-sectional image in which the corroded areas have been colored. This is an example of a cross-sectional image of a pipe in which void-like corrosion has occurred in the thickened part of the wall. This is an example of a cross-sectional image in which polar coordinate transformation has been performed. This is an example of the result of obtaining the coordinates showing the outline of the void at the pipe end and in the thickened part of the pipe wall. This is a diagram illustrating the corrosion evaluation method in Embodiment 3. This is an example of a cross-sectional image in which the corroded areas have been colored after polar coordinate transformation and binarization. This is an example of a pipe cross-sectional image in which the corroded areas have been colored. This is an example of an image in which the depth and location of corrosion holes are visualized in Embodiment 3.

[0009] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. While multiple embodiments will be described below, it has been intended from the outset that the configurations described in each embodiment can be appropriately combined. In the drawings, the same or corresponding parts are denoted by the same reference numerals, and their descriptions will not be repeated.

[0010] Embodiment 1. Embodiment 1 describes a method for evaluating corrosion when the inner surface of a pipe is locally thinned in a bowl shape.

[0011] Figure 1 is a block diagram showing the schematic configuration of a corrosion evaluation device in Embodiment 1. The corrosion evaluation device 10 comprises an operation unit 11, an input unit 12, a communication unit 13, and an information processing device 17.

[0012] The operation unit 11 is, for example, a keyboard or mouse. The input unit 12 is, for example, an input port for storing data in the storage unit 15. The communication unit 13 is, for example, a communication interface for exchanging various types of information.

[0013] The information processing device 17 processes images of the piping and performs corrosion damage evaluation. The information processing device 17 includes an output unit 14, a storage unit 15, and an information processing unit 16. The output unit 14 is, for example, a display for displaying various information.

[0014] The information processing unit 16 is, for example, a CPU (Central Processing Unit) processor, and the storage unit 15 is, for example, memory. The information processing unit 16 is the main computational unit that performs corrosion damage evaluation of piping by executing various programs. The processor performs various processes by executing programs, but some or all of these functions may be implemented using dedicated hardware circuits such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field-Programmable Gate Array).

[0015] The memory unit 15 stores a corrosion evaluation program and cross-sectional images of the pipes to be evaluated. The memory unit 15 provides a storage area for storing program code and various variables when the processor executes various programs. The memory unit 15 may be one or more non-transitory computer-readable media. Examples of memory include volatile memory such as DRAM (dynamic random access memory) and SRAM (static random access memory), or non-volatile memory such as ROM (Read Only Memory) and flash memory. The CPU executes the programs stored in the memory unit 15 to perform processing related to the evaluation of corrosion damage to the pipes.

[0016] Figure 2 is a functional block diagram of the information processing unit in Embodiment 1. The information processing unit 16 includes an image acquisition unit 21, a polar coordinate transformation unit 22, an extraction unit 23, a creation unit 24, a corrosion evaluation unit 25, a Cartesian coordinate transformation unit 26, an image creation unit 27, a corrosion hole counting unit 28, and a maximum depth evaluation unit 29.

[0017] The image acquisition unit 21 acquires a cross-sectional image of the pipe to be evaluated from the storage unit 15. The polar coordinate transformation unit 22 finds the coordinates of the center of the pipe in the acquired cross-sectional image and performs polar coordinate transformation of the image based on this point. Using the transformation result, the polar coordinate transformation unit 22 creates an image in which the distance and declination from the coordinates of the center of the pipe are used as axes. The extraction unit 23 performs binarization processing of the cross-sectional image after polar coordinate transformation and then extracts the coordinates of the inner and outer surfaces of the corroded pipe. The creation unit 24 creates the coordinates of the inner and outer surfaces of the pipe before corrosion.

[0018] The corrosion evaluation unit 25 identifies areas located between the coordinates of the inner and outer surfaces of the pre-corrosion pipe created by the creation unit 24, and which are displayed in a different color from the pipe portion in the binarized image of the cross-sectional image after polar coordinate transformation, as corrosion areas. The corrosion evaluation unit 25 then compares the coordinates of the corrosion areas with the coordinates of the pipe surface to be evaluated, and stores the maximum difference in the distance direction from the coordinates of the center of the pipe as the corrosion depth, as numerical data for each angle (if the image is divided into M parts in the angle direction and polar coordinate transformation is performed, the number of rows of numerical data will be M) in the storage unit 15.

[0019] The Cartesian coordinate transformation unit 26 colors the corroded areas in the cross-sectional image after polar coordinate transformation based on the evaluation results from the corrosion evaluation unit 25, converts the colored image to Cartesian coordinates, stores it in the storage unit 15, and displays it on the output unit 14.

[0020] The image creation unit 27 obtains numerical data representing the corrosion damage evaluation results for each cross-sectional image of the same pipe from the storage unit 15. The image creation unit 27 then extracts a column of numerical data representing the corrosion depth (number of pixels) at each angle and concatenates them in the column direction to obtain a single numerical data (an M x N matrix if there are N cross-sectional images to be analyzed). Furthermore, the image creation unit 27 creates colored image data based on the magnitude relationships of the numerical values, adjusts the aspect ratio to match the aspect ratio of the unfolded view of the pipe evaluation area, and stores it in the storage unit 15.

[0021] The corrosion hole counting unit 28 counts corrosion holes based on the numerical data created by the image creation unit 27. The maximum depth evaluation unit 29 determines the maximum corrosion depth (number of pixels) for each corrosion hole counted by the corrosion hole counting unit 28, and calculates the actual corrosion depth by multiplying this by the length per unit pixel in the cross-sectional image after polar coordinate transformation, and stores this as numerical data in the storage unit 15.

[0022] Figure 3 is a schematic diagram showing the dimensions of the piping, the imaging interval of the cross-sectional images, and the imaging range in the longitudinal direction in Embodiment 1. The imaging interval d of the cross-sectional images is a constant value, r represents the radius of the inner surface of the piping, and w represents the imaging range in the longitudinal direction. The number of cross-sectional images is indicated by N, and each cross-sectional image is numbered sequentially from 1, 2, 3..., N-1, N from the edge of the imaging range, and cross-sectional images P(1) to P(N) are stored in the storage unit 15.

[0023] Figure 4 is a flowchart showing the method for analyzing a cross-sectional image in Embodiment 1. The corrosion evaluation method of Embodiment 1 includes the steps of acquiring a cross-sectional image of a pipe (S1) and evaluating the cross-sectional image to obtain radial corrosion information indicating the degree of radial corrosion of the pipe in the cross-sectional image (S8). Furthermore, the corrosion evaluation method includes the steps of determining the center coordinates of the minimum circumscribed circle for the pipe portion in the cross-sectional image of the pipe and transforming the cross-sectional image of the pipe into polar coordinates based on the center coordinates (S2, S3, S4). Furthermore, the corrosion evaluation method includes the steps of creating coordinates of the initial shape of the inner surface or outer surface of the pipe in the cross-sectional image of the pipe after polar coordinate transformation (S5, S6) and extracting areas as corrosion areas in the binarized image of the cross-sectional image of the pipe after polar coordinate transformation, where the area is located between the coordinates of the initial shape of the inner surface and the coordinates of the initial shape of the outer surface of the pipe at the same declination angle and is displayed with a different color or brightness than the pipe portion.

[0024] More specifically, when the information processing unit 16 executes the analysis program, in step S1 the information processing unit 16 acquires the cross-sectional image stored in the storage unit 15.

[0025] Figure 5 is an example of a cross-sectional image of a pipe with a corroded interior. In step S2, the information processing unit 16 sets a threshold for the brightness value, and performs a binarization process on this image, making areas above the threshold white and areas below the threshold black, thereby extracting the pipe section. Then, in step S3, the information processing unit 16 finds the smallest circumscribed circle for the binarized image and obtains the coordinates of its center O. Furthermore, in step S4, the information processing unit 16 uses the obtained coordinates of the center O as the coordinates representing the center of the pipe, and performs a polar coordinate transformation of the image based on this to create an image represented by the distance r and deflection angle θ from the coordinates of the center O of the pipe.

[0026] Figure 6 is an example of a cross-sectional image after polar coordinate transformation. In Figure 6, the horizontal axis represents the distance r from the center of the pipe, and the vertical axis represents the deflection angle θ. The left side of the pipe section represents the inner surface of the pipe, and the right side represents the outer surface of the pipe. In step S5, the information processing unit 16 extracts the outer and inner shapes of the pipe from the cross-sectional image after polar coordinate transformation. The information processing unit 16 sets a threshold for the brightness value of the cross-sectional image after polar coordinate transformation, and binarizes the cross-sectional image after polar coordinate transformation so that areas above the threshold are represented in white and areas below the threshold are represented in black. Then, the information processing unit 16 obtains the coordinates indicating the contour of the pipe cross-section from the boundary between white and black and stores them in the storage unit 15. Figure 7 is an example of the result of obtaining the coordinates of the contour of the pipe cross-section.

[0027] In step S6, the information processing unit 16 creates the initial inner surface shape. The coordinates of the inner surface of the pipe before corrosion in the cross-sectional image after polar coordinate transformation can be calculated, for example, from the length per unit pixel in the distance direction from the center O of the pipe and the inner diameter of the pipe. Alternatively, they can be calculated from the length per unit pixel in the distance direction from the center O of the pipe, the coordinates representing the outer surface of the pipe, and the thickness of the pipe. Furthermore, the coordinates of the corroded inner surface of the pipe can also be obtained by processing in various ways. For example, the coordinates obtained by plotting the coordinates of the inner surface of the pipe with the angular deviation direction on the horizontal axis and the distance direction from the center O of the pipe on the vertical axis, and then smoothing the data using the simple moving average method or the Savitzkey-Golay method, can be used as the inner surface of the pipe before corrosion. Any of the above methods can be used to determine the coordinates of the inner surface of the pipe before corrosion in step S6.

[0028] Figure 8 illustrates the corrosion evaluation method in Embodiment 1. The dashed lines show the coordinates of the inner surface of the pipe before corrosion, and the solid lines show the coordinates of the outer surface of the pipe. The white and gray areas are the parts that appear white and black, respectively, when the cross-sectional image after polar coordinate transformation is binarized. The white areas represent the pipe, and the gray areas represent the space inside or outside the pipe.

[0029] In step S7, the information processing unit 16 extracts the corroded areas. The information processing unit 16 identifies the corroded areas as locations that are situated between the coordinates r1 of the inner surface of the pipe before corrosion and the coordinates r2 of the outer surface of the pipe, and that are displayed in a different color (or brightness) than the pipe portion in the binarized image (Figure 8) of the cross-sectional image after polar coordinate transformation.

[0030] The corrosion evaluation method shown in Embodiment 1 further includes a corrosion evaluation step (S8) in which the corrosion depth is determined for each deviation angle by comparing the coordinates of the corrosion location with the coordinates of the initial shape of the inner or outer surface of the pipe, and stored as numerical data. In step S8, the information processing unit 16 compares the coordinates of the corrosion location with the coordinates of the inner surface of the pipe before corrosion, calculates the maximum difference in the distance direction from the center of the pipe for each deviation angle θ, and stores the calculation result in the storage unit 15 in step S9. This calculation result represents the corrosion depth L of the pipe at each deviation angle θ.

[0031] Figure 9 shows an example of numerical data representing the corrosion depth L for each circumferential angle θ(1) to θ(M). By multiplying this numerical data by the length Δr per unit pixel in the distance direction from the center of the pipe in the cross-sectional image after polar coordinate transformation, the corrosion depth can be calculated.

[0032] The corrosion evaluation method shown in Embodiment 1 further comprises the step (S10) of creating a first image in which the color or brightness of the corroded area is changed in the pipe image after polar coordinate transformation, and converting the data of the first image to Cartesian coordinates to generate a corrosion cross-sectional image in which the corroded area is highlighted relative to the pipe cross-sectional image. In step S10, the information processing unit 16 can visualize the corroded area by coloring the area enclosed by the coordinates of the inner surface of the pipe before and after corrosion with an arbitrary color or changing the brightness to an arbitrary value.

[0033] Figure 10 is an example of a cross-sectional image in which corroded areas have been colored after polar coordinate transformation and binarization. The dashed line represents the coordinates of the inner surface of the pipe before corrosion. In step S11, the information processing unit 16 converts Figure 10 to Cartesian coordinates, thereby obtaining an image that visualizes the corroded areas in the pipe. Figure 11 is an example of a cross-sectional image of a pipe in which corroded areas have been colored. By following the above procedure, the corrosion depth in each cross-sectional image can be evaluated.

[0034] Figure 12 is a flowchart showing a method for counting corrosion locations and a method for evaluating the maximum corrosion depth of each corrosion location.

[0035] The corrosion evaluation method of Embodiment 1 acquires multiple corrosion cross-sectional images of the same pipe at different locations along the longitudinal direction of the pipe, and multiple numerical data indicating the corrosion depth corresponding to each of the multiple corrosion cross-sectional images, by processing steps S1 to S10 in Figure 4. The corrosion evaluation method further includes the step (S21) of extracting data in a column with the same deflection angle from the multiple numerical data and concatenating them in the row direction, and the step (S22) of adjusting the aspect ratio to create an unfolded planar image.

[0036] The corrosion evaluation method shown in Embodiment 1 further comprises the step (S23) of counting the corrosion locations in the unfolded planar image. The corrosion evaluation method further comprises the step (S24) of comparing the corrosion depth of each of the multiple corrosion locations in the unfolded planar image and evaluating the maximum corrosion depth.

[0037] More specifically, in step S21, the information processing unit 16 obtains the analysis results of N cross-sectional images of the same pipe stored in the memory unit, and for each s-th (s=1 to N) cross-sectional image, it generates a series of numerical data L representing the corrosion depth at each deflection angle θ. ｓ， θ (1) ~L ｓ， θ (M) The data is extracted and concatenated in the numerical data row direction, starting with the smallest image number. Figure 13 is an example of numerical data representing the corrosion depth L in all cross-sectional images.

[0038] In step S22, the information processing unit 16 colors the pixels based on the numerical values ​​input to this data and outputs it as image data, thereby making it possible to visualize the distribution of corrosion holes on the inner surface of the pipe. However, since the aspect ratio of this image data is M:N, it is adjusted to 2πr:w. Here, r represents the radius of the inscribed circle shown in Figure 3, and w represents the length in the longitudinal direction shown in Figure 3. Regarding the coloring method, the brightness may be changed in grayscale according to the corrosion depth, or different colors may be used for each range of corrosion depth. For the sake of simplicity in the following explanation, changing the brightness to make white or gray appear black may also be referred to as "coloring".

[0039] Figure 14 is an example of an image visualizing the depth and location of corrosion holes. When evaluating corrosion damage locations using the method described in Japanese Patent Publication No. 6887535 (Patent Document 1), there was a problem in that the distribution of corrosion damage in the longitudinal direction of the pipe could not be evaluated. In this embodiment, the distribution of corrosion damage in the longitudinal direction of the pipe can be evaluated. In Figure 14, pixels with smaller brightness values ​​(darker) indicate deeper corrosion. The information processing unit 16 considers any area where corrosion continues vertically, horizontally, or diagonally as one corrosion location, counts the number of such locations in step S23, and determines the maximum corrosion depth in each corrosion hole in step S24. At that time, the number of pixels is converted to the actual length to determine the depth. This allows for statistical analysis of the corrosion depth. For example, the degree of corrosion progression can be visualized by creating a histogram.

[0040] Figure 15 shows an example of a histogram of the maximum corrosion depth at each corrosion hole. Statistical analysis of corrosion depth is not limited to histograms; generally known statistical methods may also be used.

[0041] Embodiment 2. Embodiment 1 described a method for evaluating corrosion when the inner surface of a pipe is locally thinned in a bowl shape. Embodiment 2 describes a method for evaluating corrosion when the outer surface of a pipe is locally thinned in a bowl shape.

[0042] FIG. 16 is a schematic diagram showing the dimensions of a pipe, the imaging interval of cross-sectional images, and the imaging range in the longitudinal direction in Embodiment 2. In FIG. 16, the radius of the outer surface of the pipe is r. When the imaging range in the longitudinal direction is w and the imaging interval of cross-sectional images is d, the number N of cross-sectional images P(1) to P(N) is expressed as N = w / d + 1.

[0043] FIG. 17 is a flowchart showing a method for analyzing cross-sectional images in Embodiment 2. When the analysis program is executed in the information processing unit 16, in step S31, the information processing unit 16 acquires the cross-sectional images stored in the storage unit 15.

[0044] FIG. 18 is an example of a cross-sectional image of a pipe with a corroded outer surface. In step S32, the information processing unit 16 sets a threshold value for the luminance value, performs binarization of this image by setting the portions above the threshold value to white and the portions below the threshold value to black, and extracts the pipe portion. Then, in step S33, the information processing unit 16 obtains the minimum circumscribed circle for the binarized image and acquires the coordinates of its center O. Further, in step S34, the information processing unit 16 uses the obtained coordinates of the center O as the coordinates representing the center of the pipe, and based on this, performs polar coordinate transformation of the image to create an image represented by the distance r from the coordinates of the center O of the pipe and the declination angle θ as axes.

[0045] FIG. 19 is an example of a cross-sectional image after polar coordinate transformation. In FIG. 19, the horizontal axis indicates the distance from the coordinates of the center of the pipe, and the vertical axis indicates the declination angle θ. Also, the left side of the pipe portion represents the inner surface of the pipe, and the right side of the pipe portion represents the outer surface of the pipe. In step S35, the information processing unit 16 also sets a threshold value for the luminance value for the cross-sectional image after polar coordinate transformation, performs binarization by setting the portions above the threshold value to white and the portions below the threshold value to black for the cross-sectional image after polar coordinate transformation, and then acquires the coordinates indicating the contour portion of the pipe cross-section and stores them in the storage unit 15. FIG. 20 is an example of the result of acquiring the coordinates of the contour portion of the pipe cross-section.

[0046] In step S36, the information processing unit 16 creates an initial inner surface shape. The coordinates of the outer surface of the pipe before corrosion in the cross-sectional image after polar coordinate conversion can be obtained, for example, by calculating the length per unit pixel in the distance direction from the center of the pipe and from the outer diameter of the pipe. It is also possible to calculate from the length per unit pixel in the distance direction from the center of the pipe, the coordinates representing the inner surface of the pipe, and the thickness of the pipe. Furthermore, it can also be obtained by applying a process to the coordinates of the corroded outer surface of the pipe. The coordinates obtained by smoothing the data obtained by plotting the coordinates of the inner surface of the pipe with the declination direction as the horizontal axis and the distance direction from the center of the pipe as the vertical axis by the simple moving average method or the Savitzkey-Golay method may be used as the outer surface of the pipe before corrosion. Any of the above methods may be used to obtain the coordinates of the outer surface of the pipe before corrosion in step S36.

[0047] FIG. 21 is a diagram for explaining the corrosion evaluation method in the second embodiment. The broken line indicates the coordinates of the outer surface of the pipe before corrosion, and the solid line indicates the coordinates of the inner surface of the pipe. Also, the white part and the gray part are the parts displayed in white and black, respectively, when the cross-sectional image after polar coordinate conversion is binarized. The white part indicates the pipe, and the gray part indicates the inside or outside of the pipe.

[0048] In step S37, the information processing unit 16 extracts the corroded part. The information processing unit 16 determines the corroded part as the part located between the coordinates r2 of the outer surface of the pipe before corrosion and the coordinates r1 of the inner surface of the pipe, and in the image (FIG. 21) obtained by binarizing the cross-sectional image after polar coordinate conversion, the part displayed in a color (or luminance) different from that of the pipe part. In step S38, the information processing unit 16 compares the coordinates of the corroded part with the coordinates of the outer surface of the pipe before corrosion, calculates the maximum value of the difference in the distance direction from the center of the pipe for each declination θ, and stores the calculation result in the storage unit 15 in step S39. This calculation result represents the corrosion depth of the pipe at each declination θ. In step S40, the information processing unit 16 visualizes the corroded part by coloring the part surrounded by the coordinates of the inner surface of the pipe before and after corrosion with an arbitrary color or changing it to an arbitrary luminance.

[0049] Figure 22 is an example of a cross-sectional image in which corroded areas have been colored after polar coordinate transformation and binarization. The dashed line represents the coordinates of the inner surface of the pipe before corrosion. In step S41, the information processing unit 16 transforms Figure 22 into Cartesian coordinates to obtain an image that visualizes the corroded areas in the pipe. Figure 23 is an example of a cross-sectional image of the pipe in which corroded areas have been colored. By following the above procedure, the corrosion depth in each cross-sectional image can be evaluated. The acquisition of numerical data representing the corrosion depth L at each deviation angle θ in the circumferential direction is the same as in Embodiment 1. The corrosion depth L at each deviation angle θ can be calculated by multiplying the length per unit pixel in the distance direction from the center of the pipe in the cross-sectional image after polar coordinate transformation by the number of pixels in the image. Furthermore, the method for counting the corroded areas and the method for evaluating the maximum corrosion depth of each corroded area are also the same as in Embodiment 1.

[0050] Embodiment 3. Embodiment 2 described a method for evaluating corrosion when the outer surface of a pipe is locally thinned in a bowl shape. Embodiment 3 describes a method for evaluating corrosion when the corrosion appears as voids in the pipe wall thickness. For example, in a corrosion form called ant nest corrosion, the corrosion progresses in a filamentous manner from minute pores on the material surface, so the corrosion appears as voids in the cross-sectional image.

[0051] Figure 24 is an example of a cross-sectional image of a pipe where void-like corrosion has occurred in the thicker wall section. A threshold value is set for the brightness value, and areas above the threshold are treated as white, while areas below the threshold are treated as black. This image is then binarized to find the smallest circumscribed circle and obtain the coordinates of its center. These coordinates are used to represent the center O of the pipe, and by performing a polar coordinate transformation of the image based on these coordinates, an image is created in which the distance r and deflection angle θ from the coordinates of the pipe center O are used as axes.

[0052] Figure 25 is an example of a cross-sectional image after polar coordinate transformation. In Figure 25, the horizontal axis represents the distance from the center O of the pipe, the vertical axis represents the deflection angle θ, the left side of the pipe section represents the inner surface of the pipe, and the right side of the pipe section represents the outer surface of the pipe. A threshold value is set for the brightness value, and the cross-sectional image after polar coordinate transformation is binarized so that areas above the threshold are white and areas below the threshold are black. Then, the coordinates indicating the outlines of the gaps at the pipe ends and in the pipe wall thickness sections are obtained and stored in the memory unit.

[0053] Figure 26 is an example of the results obtained from acquiring coordinates that show the contour of the gap at the end of the pipe and in the pipe wall thickness. The method for determining the coordinates representing the outer surface of the pipe before corrosion is the same as in Embodiment 2. That is, the schematic diagram showing the imaging interval and imaging range in the longitudinal direction of the cross-sectional image, and the flowchart showing the method for analyzing the cross-sectional image are the same as in Figures 16 and 17 of Embodiment 2, so the explanation will not be repeated here.

[0054] Figure 27 illustrates the corrosion evaluation method in Embodiment 3. The dashed lines represent the coordinates of the outer surface of the pipe before corrosion, and the solid lines represent the coordinates of the inner surface of the pipe. The white and gray areas are the parts that appear white and black, respectively, when the cross-sectional image after polar coordinate transformation is binarized. In Figure 27, the white areas represent the pipe, and the gray areas represent the inside or outside of the pipe or the void in the pipe wall thickness. Corrosion is defined as a location between the coordinates of the outer surface of the pipe before corrosion and the coordinates of the inner surface of the pipe, and which is displayed in a different color from the pipe portion in the binarized image of the cross-sectional image after polar coordinate transformation. The coordinates of the corrosion are compared with the coordinates of the outer surface of the pipe before corrosion, and the maximum difference in the distance direction from the center of the pipe is calculated for each deflection angle θ, and the calculation result is stored in the memory. This calculation result represents the corrosion depth of the pipe (the diameter of the corroded part of the pipe) at each deflection angle θ.

[0055] Figure 28 is an example of a cross-sectional image in which corroded areas have been colored after polar coordinate transformation and binarization. The dashed line represents the coordinates of the outer surface of the pipe before corrosion. By transforming this figure into Cartesian coordinates, an image visualizing the corroded areas in the pipe can be obtained. Figure 29 is an example of a cross-sectional image of the pipe after being transformed into Cartesian coordinates with the corroded areas colored.

[0056] The above procedure allows for the evaluation of corrosion depth in each cross-sectional image. The acquisition of numerical data representing the corrosion depth at each circumferential angle θ is the same as in Embodiment 1. By multiplying the length per unit pixel in the distance direction from the center of the pipe in the cross-sectional image after polar coordinate transformation by the number of pixels in the image, the corrosion depth (radial dimension) at each circumferential angle θ can be calculated. Furthermore, the method for counting corrosion locations and evaluating the maximum corrosion depth at each corrosion location are also the same as in Embodiment 1.

[0057] Figure 30 is an example of an image visualizing the depth and location of corrosion holes in Embodiment 3. In this figure, pixels with lower brightness values ​​indicate deeper corrosion. Corrosion may progress linearly from the center of the circular pipe, or it may progress diagonally from the center of the circular pipe. A single corrosion area is considered to be a series of corrosion areas in any direction (vertical, horizontal, or diagonal), and the number of such areas is counted. By converting the number of pixels to the actual length, the maximum corrosion depth at each corrosion hole is determined.

[0058] According to the corrosion evaluation method described in Embodiments 1 to 3, the corrosion depth in the thickness direction of the metal pipe can be evaluated from radiographic images, the number of corrosion holes in the radiographic imaging range in the longitudinal direction of the metal pipe can be counted, and the maximum corrosion depth at each corrosion hole can be determined. This allows for statistical analysis of the corrosion depth.

[0059] It should be noted that the program for causing a computer to execute the corrosion evaluation method described in any of the embodiments 1 to 3 above, the information processing apparatus comprising a storage device that records the program and a processor that executes the program, and the recording medium that records the program are intended to be other aspects of the embodiments.

[0060] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the present invention is indicated by the claims rather than by the description of the embodiments above, and all modifications within the meaning and scope of the claims are intended to be included.

[0061] 10 Corrosion evaluation device, 11 Operation unit, 12 Input unit, 13 Communication unit, 14 Output unit, 15 Storage unit, 16 Information processing unit, 17 Information processing unit, 21 Image acquisition unit, 22 Polar coordinate transformation unit, 23 Extraction unit, 24 Creation unit, 25 Corrosion evaluation unit, 26 Cartesian coordinate transformation unit, 27 Image creation unit, 28 Corrosion hole counting unit, 29 Maximum depth evaluation unit.

Claims

1. A corrosion evaluation method for evaluating corrosion of piping, comprising the steps of: acquiring a cross-sectional image of the piping; and evaluating the cross-sectional image to obtain radial corrosion information indicating the degree of radial corrosion of the piping in the cross-sectional image.

2. A corrosion evaluation method according to claim 1, comprising the steps of: determining the center coordinates of the smallest circumscribed circle for the piping portion in the cross-sectional image, and transforming the cross-sectional image into polar coordinates based on the center coordinates; creating coordinates for the initial shape of the inner surface or outer surface of the piping in the cross-sectional image after the polar coordinate transformation; and, in the binarized image of the cross-sectional image after the polar coordinate transformation, extracting areas as corrosion areas that are displayed with a different color or brightness from the piping portion at the same angle between the coordinates for the initial shape of the inner surface of the piping and the coordinates for the initial shape of the outer surface of the piping.

3. The corrosion evaluation method according to claim 2, wherein the step of evaluating the cross-sectional image involves comparing the coordinates of the corroded area with the coordinates of the initial shape of the inner surface or outer surface of the pipe to determine the corrosion depth for each angle and save it as numerical data.

4. The corrosion evaluation method according to claim 2 or 3, further comprising the steps of creating a first image in which the color or brightness of the corroded area is changed in the piping image after polar coordinate transformation, and converting the data of the first image to Cartesian coordinates to generate a corrosion cross-sectional image in which the corroded area is emphasized relative to the cross-sectional image.

5. A corrosion evaluation method according to claim 3, further comprising the steps of: obtaining a plurality of corrosion cross-sectional images of the same pipe at different locations in the longitudinal direction of the pipe and a plurality of numerical data indicating the corrosion depth corresponding to each of the plurality of corrosion cross-sectional images; and extracting a row of data with the same deflection angle from the plurality of numerical data for each deflection angle, concatenating them in the row direction and adjusting the aspect ratio to create an unfolded planar image.

6. The corrosion evaluation method according to claim 5, further comprising the step of counting the number of corroded areas in the unfolded planar image.

7. The corrosion evaluation method according to claim 5 or 6, further comprising the step of comparing the corrosion depths of each of the multiple corrosion locations in the unfolded plan image and evaluating the maximum corrosion depth.

8. A recording medium that stores a program for causing a computer to execute the corrosion evaluation method described in any one of claims 1 to 7.

9. A program that causes a computer to execute the corrosion evaluation method described in any one of claims 1 to 7.

10. A corrosion evaluation device for evaluating corrosion of piping, comprising: an image acquisition unit for acquiring a cross-sectional image of the piping; and a corrosion evaluation unit for evaluating the cross-sectional image to obtain radial corrosion information indicating the degree of radial corrosion of the cross-sectional image acquired by the image acquisition unit.

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