Automatic determination system for the degree of deterioration in anchor bolts

Abstract

To provide an automatic determination system for the degree of deterioration in an anchor bolt, which enables an inspector to easily determine a deterioration state of an anchor bolt on site.SOLUTION: An automatic determination system for the degree of deterioration in an anchor bolt automatically determines the degree of deterioration of an anchor bolt based on a developed view that is prepared in advance and shows the degree of deterioration of the anchor bolt. The developed view is created based on data obtained by detecting the degree of deterioration of the anchor bolt with an ultrasonic probe, and shows the degree of deterioration and the position of the anchor bolt. In the developed view, a sound part of the bolt and a half-depth missing part of a screw thread are separated by a predetermined color. From the developed view, preprocessing of reading a ratio and an occurrence position according to a predetermined color is performed, and the degree of deterioration of the anchor bolt is automatically determined based on the preprocessing results (S31 to S35).SELECTED DRAWING: Figure 16

Description

Technical Field

[0001] This invention relates to an automatic determination system for the degree of deterioration of anchor bolts, and more particularly to an automatic determination system for the degree of deterioration of anchor bolts that enables on-site inspectors to determine the deterioration state of anchor bolts.

Background Art

[0002] Conventionally, an ultrasonic flaw detection method for anchor bolts has been described, for example, in Japanese Patent No. 6088088 (Patent Document 1). According to this publication, an ultrasonic flaw detector for anchor bolts inspects the corroded part of the anchor bolt 27 using an ultrasonic probe. The ultrasonic flaw detector for anchor bolts can be attached to the head of the anchor bolt and includes a probe attachment part having a cylindrical shape with an inclined surface at its top where an ultrasonic probe can be attached, a probe rotation jig that rotatably holds the probe attachment part, and a digital ultrasonic flaw detector that receives the reflected echo of the ultrasonic flaw detection signal from the ultrasonic probe and determines the deterioration of the anchor bolt.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In Patent Document 1, the deterioration of the anchor bolt was determined by receiving the reflected echo of the ultrasonic flaw detection signal from the ultrasonic probe. In such a conventional deterioration determination system for anchor bolts, skilled technicians judged the degree of thread loss and the like from the inspection results.

[0005] As a method for determining the deterioration of an anchor bolt, even if a developed drawing of the bolt can be created from the inspection results and the occurrence position and degree of damage can be illustrated, there has been a problem that the determination of the state of the anchor bolt (sound, initially defective bolt, bolt with damage occurrence, dangerous bolt) can only be done by a skilled technician.

[0006] This invention was made to solve the above problems, and an object thereof is to provide an automatic determination system for the degree of deterioration in an anchor bolt that allows an inspector on site to easily determine the deterioration state of the anchor bolt.

Means for Solving the Problems

[0007] The automatic determination system for the degree of deterioration in an anchor bolt according to this invention automatically determines the degree of deterioration of the anchor bolt based on a developed drawing showing the degree of deterioration of the anchor bolt created in advance. The developed drawing is created based on data detected by an ultrasonic probe of the degree of deterioration of the anchor bolt, shows the degree of deterioration of the anchor bolt and its position and size, and in the developed drawing, the sound part of the bolt, the half-depth defect part of the thread, and the full-depth defect part of the thread are separated by a predetermined color. From the developed drawing, according to the predetermined color, the ratio and occurrence position are read, preprocessing is performed, and based on the result of the preprocessing, the degree of deterioration of the anchor bolt is automatically determined.

[0008] Preferably, the step of automatically determining the degree of deterioration of the anchor bolt is performed using fuzzy theory.

[0009] The degree of deterioration of the anchor bolt may be separated into three or more levels such as sound and dangerous, or sound, caution, and dangerous.

[0010] The step of determining the degree of deterioration of the anchor bolt performed using fuzzy theory may be divided into a first step and a second step.

[0011] In the first step, it is preferable to determine the deterioration of sound and dangerous, and in the second step, to determine the deterioration at the caution level.

Effects of the Invention

[0012] According to the present invention, measurement data is associated with the degree of deterioration based on the judgment of a skilled technician, and if there is measurement data, the degree of deterioration is automatically judged.

[0013] As a result, it is possible to provide an automatic determination system for the degree of deterioration in an anchor bolt, which enables an inspector to determine the deterioration state of the anchor bolt on site.

Brief Description of the Drawings

[0014]

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Embodiments for Carrying Out the Invention

[0015] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. First, the data used for creating the automatic determination system for the degree of deterioration of the anchor bolts this time will be described. The data used this time is as shown in FIG. 1, and is a developed view in which the level of deterioration of the anchor bolts measured by the ultrasonic probe of the anchor bolt deterioration determination device is represented by a line for each position. The horizontal axis is the angle in the circumferential direction of the anchor bolt, and the vertical axis indicates the distance in the depth direction.

[0016] Here, the image shown in FIG. 1 originally includes blue (sound part), yellow (caution level 1), white (caution level 2), and red (dangerous part), but here, each is represented by four lines: a thin line, a dotted line, a dashed-dotted line, and a thick line. Here, the thin line is the beam path of the sound part echo, the dotted line indicates a location where a defect about half the depth of the thread is progressing, the dashed-dotted line indicates a location where a defect about the depth of the thread is progressing, and the thick line indicates a location where a defect is progressing beyond the depth of the thread.

[0017] Here, in the figure, it is represented by four lines: a thin line, a dotted line, a dashed-dotted line, and a thick line, but actually, the location indicated by the dashed-dotted line is displayed in white as the measurement result.

[0018] Referring to FIG. 1, for example, the nominal diameter of the screw is M22. It shows which of the data of sound, caution level 1, caution level 2, danger, and inspection failure this data corresponds to.

[0019] Figure 2 is a diagram showing an overview of the number of data used in this embodiment among such image data. In Figure 2, for the measurement data of M22, the measurement data of M24, the measurement data of M27, the measurement data of M30, and the measurement data of M36, a table showing which data is sound, attention level 1, attention level 2, danger, or inspection failure, a bar graph of the number of data shown above the table, and on the right side thereof, a total aggregation for each level of bolt deterioration such as soundness, and a pie chart representing the breakdown of each level based on the aggregation are displayed.

[0020] Since the measurement ranges were different for each nominal diameter of the screw, it was necessary to homogenize these data according to certain rules in order to conduct development by summarizing them. Also, there were a total of 404 images, and the results of sorting these images by visual inspection by an experienced person into four levels: sound level, attention level 1, attention level 2, and danger level were used as teacher data. As a result, as shown in Figure 2, approximately 60% was the sound level, approximately 25% was attention level 1, approximately 10% was attention level 2, and approximately 10% was the danger level.

[0021] Next, an explanation will be given regarding the aggregation of the number of colors in the image. Figure 3 is a diagram (A) of the result image and a diagram (B) schematically showing the intensity of the measurement waveform and the color corresponding to the intensity for creating the result image.

[0022] First, the inventors considered aggregating the number of colors in the image. That is, initially, it was thought that deterioration determination might be possible by aggregating the number of blue lines, yellow lines, red lines, and white lines in the image. However, as shown in Figure 3(B), since the white lines are the same color as the background color and are difficult to separate, aggregating the quantity of white lines became a problem.

[0023] First, it was considered to convert only the white lines to another color by information processing. However, since a helically processed screw was being measured, the lines displayed in the image obtained from the measurement data also had an inclination, and it was concluded that it was difficult to identify the white lines by information processing.

[0024] Another proposal was to change the background color to another color. However, white indicates a low echo height. Although there is data measured for the white parts other than those shown as lines for the threads, they appear white because the echo is low. Therefore, even if the background color is changed, the white lines will become the same color as the changed background color this time, resulting in the same problem.

[0025] Also, since this low echo level may contain noise, it was found that it is difficult to identify only the threads from the data with a low echo level and display them as lines.

[0026] Based on the above, it was determined that it is difficult to accurately totalize the white color, and the amount of the white lines was estimated by estimating the white lines. Also, as methods, two methods were considered: a method of estimating the white lines by dilation and contraction, which is an image processing method, and a method of estimating the white lines by detecting the thread lines by straight line detection.

[0027] Next, the estimation of the white lines by this image processing will be explained. Here, as image processing methods, binarization, dilation processing, contraction processing, or opening processing and labeling processing that combine these were used to attempt to estimate the white lines.

[0028] Figure 4 shows the concept of dilation and contraction. As shown in Figure 4, in the dilation process, the target pixel is replaced with white, and in the contraction process, the target pixel is replaced with black. To perform the dilation process or the contraction process, it is necessary to set the number of processing times. However, if the number of times is set to fill only the white parts between the thread lines, the parts estimated as white lines will remain. Also, although the thread lines are inclined, since the intervals between the thread lines are almost parallel and at almost a constant interval, it can be seen that the intervals between the thread lines can be calculated.

[0029] Next, the line interval will be explained. By appropriately setting the line interval (in pixel units), the number of dilation and contraction processes was determined, and this was done to separate the white of the background color from the white of the lines. That is, the line interval and the number of dilation and contraction processes are synonymous. Figure 5 is a diagram for explaining the reason for setting the line interval.

[0030] As shown in Fig. 5, lines can be extracted only when the dilation and erosion processing is performed appropriately (in the figure, for the case of the right horizontal arrow). On the other hand, when the dilation and erosion processing is performed more times than reaching the next line (in the figure, for the case of the right diagonal upward arrow), the line information disappears. Conversely, when the dilation and erosion processing is performed fewer times than reaching the next line (in the figure, for the case of the right diagonal downward arrow), the background color remains as noise. And even if the labeling process is performed to enclose the line area with the result when the line interval is not specified appropriately, the accurate line area cannot be obtained.

[0031] To verify this estimation method, Fig. 6 shows the results of applying it to three colors, red, yellow, and blue, for which the number of colors is determined. This figure shows the number of each color obtained by this estimation method on the horizontal axis and the number of each color measured on the vertical axis. Since the correlation coefficient obtained from this figure was 0.996, it shows a very strong positive correlation, and it was found that the estimation of the white line by this method is reasonable. Also, in order to unify the conditions, estimations were performed using the same processing for other colors.

[0032] Next, the deterioration determination based on the estimation of the number of each color will be described. Fig. 7 shows the results indicating the proportion of each color contained in each image for each level of deterioration determination based on the values obtained by the estimation of each color through image processing. Note that since the size of the determination area varies depending on the screw diameter, it is the result of normalizing by the determination area size.

[0033] Referring to Fig. 7, the vertical axis is a diagram showing the proportion of each color.

[0034] From this result, it can be seen that as the degree of deterioration progresses, the proportion of colors other than blue decreases and the proportion of yellow and white increases. However, since the proportions are not separated for each level of deterioration determination, it was considered difficult to determine the threshold and classify them.

[0035] Therefore, the deterioration determination by fuzzy inference was examined. The method of classification using the threshold value shown in the above deterioration determination is an image as shown in Fig. 8(A), where children and adults are clearly separated with the threshold value of 20 years old as the boundary. However, in reality, the boundary between children and adults is ambiguous. There are ideas that a person becomes an adult at 18 years old, and there are also ideas that a person remains a child until 22 years old. As a method of expressing such ambiguity, the concept of fuzzy inference was considered.

[0036] This fuzzy inference is a concept that expresses the above-mentioned ideas of children and adults in a form that allows both possibilities, as shown in Fig. 8(B). And the concept considered for this application is shown in Fig. 9. As shown in this figure, the horizontal axis represents the conditions under which a person judges the category, and the vertical axis represents the degree of agreement with each category.

[0037] Next, the application of fuzzy inference will be explained. When applying fuzzy inference, an expert was interviewed about what they were focusing on in the image, and it was found that they were looking at the amount and size of each color, namely red, yellow, and white, in the image. Therefore, first, for non-inspection failures, the following rules were applied to attempt to determine the deterioration level by fuzzy inference.

[0038] If the proportion of the blue line is large, the proportion of the yellow line is large, and the proportion of the white line is large, it is an inspection failure.

[0039] If the proportion of the blue line is large, the proportion of the yellow line is large, and the proportion of the white line is small, it is caution level 1.

[0040] If the proportion of the blue line is large, the proportion of the yellow line is small, and the proportion of the white line is large, it is an inspection failure.

[0041] If the proportion of the blue line is large, the proportion of the yellow line is small, and the proportion of the white line is small, it is caution level 1.

[0042] If the proportion of the blue line is small, the proportion of the yellow line is large, and the proportion of the white line is large, it is caution level 2.

[0043] If the ratio of the blue line is small, the ratio of the yellow line is large, and the ratio of the white line is small, the attention level is 2.

[0044] If the ratio of the blue line is small, the ratio of the yellow line is small, and the ratio of the white line is large, it is dangerous.

[0045] If the ratio of the red line is small, the ratio of the yellow line is small, and the ratio of the white line is small, it is sound.

[0046] Therefore, from the 397 pieces of data including 226 sound, 94 attention level 1, 39 attention level 2, and 38 dangerous, for each of the blue, yellow, and white data, the ratios for sound, attention level 1, attention level 2, and dangerous were extracted, and membership functions and decision rules for analysis were set to perform degradation determination. The results are shown in Table 1.

[0047]

Table 1

[0048] The results of determining the teacher data according to the above rules are shown in Table 2.

[0049]

Table 2

[0050] In Table 2, the "visual inspection result" is the result of a skilled technician's determination, and the "automatic determination result" is the inference result according to the above rules. Therefore, referring to Table 2, the correct rate is 76.3%, and when considering one difference as correct, it is 99.7%. Here, for example, regarding the visual inspection results, there are 225 sound ones, and in the automatic determination results, 1 case of attention level 1 is also included, so a numerical value of 225 / 226 = 99.6% is obtained.

[0051] Here, as the reason for the low accuracy, while the correct answer rates of sound / dangerous are nearly 100%, Caution 1 and Caution 2 are significantly lower at about 40% and about 15% respectively. From this, it is necessary to reexamine the membership function and the decision rule.

[0052] In the reexamination, since manual setting is difficult, optimization was introduced. The optimization targets are the vertices of the membership function and the decision rule.

[0053] For the vertices of the membership function, for blue, it was set to 101 steps from 0 - 100%, and for yellow and white, it was set to 101 steps from 0 to 10%. The decision rule was set to 4 steps of the state of the consequent part.

[0054] Therefore, the 9 data of the vertices of the three membership functions of each of the three input values, and the 27 data of the consequent part of the decision rule were set as the optimization targets. 30 pieces were randomly extracted from each state, for a total of 120 pieces as the teachers, and the correct answer rate when using each membership function and fuzzy rule was used as the evaluation value.

[0055] The judgment results are shown in Table 3.

[0056]

Table 3

[0057] The overall correct answer rate does not change significantly, but the accuracy of Caution 1 and Caution 2 has improved. On the other hand, the accuracy of the danger level has decreased significantly.

[0058] A system was constructed aiming to perform four classifications with one fuzzy rule. However, for the purpose of improving the judgment accuracy, for those judged as caution, fuzzy inference was performed again with different parameters and divided into Caution 1 and Caution 2.

[0059] The flowchart of the refined fuzzy judgment for this purpose is shown in Fig. 10.

[0060] Referring to FIG. 10, in the fuzzy determination process, the fuzzy inference is divided into three stages. First, the first fuzzy inference is performed (S11), where defective installations are separated. Then, for those that are not defective installations, the second fuzzy inference is performed (S12) to separate the sound and dangerous ones. After that, for those determined to be cautionary, the third fuzzy inference is performed (S13) to classify them into Caution 1 and Caution 2.

[0061] The determination results are shown in Table 4.

[0062]

Table 4

[0063] Here, the defective installation where the anchor bolt deterioration determination device was not correctly installed in the first fuzzy inference will be described. Since there may be cases where inaccurate images that cannot be used for deterioration determination are input due to defective installations or the like, a fuzzy theory for automatically determining such inspection defective images and the images to be determined was introduced.

[0064] The fuzzy inference for determining whether there is an inspection defect will be described. The inspection defect here mainly refers to a state where the fixing jig for attaching the sensor (probe) to the anchor bolt is not properly attached (perpendicular to the rotation axis of the anchor bolt), as shown in FIG. 11(A). Since the sensor is optimized to be able to scan the threads of the anchor bolt when properly installed, when measuring in this state, the ultrasonic wave transmitted from the sensor may not hit the threads and the reflected echo may be low. Note that on the opposite side of 180 degrees, the ultrasonic wave hits at a shorter distance than expected, so the reflected echo tends to be high.

[0065] When the obtained reflected echo is plotted in a figure, since the reflected echo is small as shown in FIG. 11(B), it is displayed in white. Here, if there is no concept of inspection defect and it is applied to the four categories of deterioration determination, it will be determined as dangerous. Therefore, a mechanism for determining whether there is an inspection defect by fuzzy inference was constructed.

[0066] It is said that skilled technicians often make judgments based on the white shape indicating the smallest reflection echo and the red shape indicating the largest reflection echo when determining whether there is a defective inspection. Therefore, the white and red shapes were constructed by focusing on the width and height.

[0067] The results are shown in Table 5, the rules at that time are shown in Table 6, and the membership functions at that time are shown in Figure 12.

[0068]

Table 5

[0069]

Table 6

[0070] For the first fuzzy inference, the membership function is as shown in Figure 13, and the fuzzy rule is as shown in Table 7. Also, for the second fuzzy inference, the membership function is as shown in Figure 14, and the fuzzy rule is as shown in Table 8.

[0071]

Table 7

[0072]

Table 8

[0073] The preprocessing flowchart created based on the above points is shown in Fig. 15. Referring to Fig. 15, first, an expanded view representing the level of deterioration of the anchor bolts shown in Fig. 1 with lines for each position is input. Based on this, a waveform portion as shown in Fig. 3(B) is extracted (S21), and as preprocessing, lines of each color are emphasized using image processing techniques (S22 - S23). This is performed a set number of times. Then, opening processing (S25), labeling processing (S26), and processing to obtain information such as the color of the line of interest (S27) are carried out to extract information such as area from the emphasized data, and this is done for all colors.

[0074] Here, the opening processing refers to a process of performing contraction processing and dilation processing once each to remove noise, and the labeling processing refers to a process of specifying the position and area for each block.

[0075] Here, the data obtained from the total color count is, for example, data such as the blue area ratio being 99.971, the yellow area being 0.066, the white area being 0.029, and the red area being 0.00.

[0076] Next, the fuzzy determination process will be described. Fig. 16 is a detailed flowchart explaining the process in the fuzzy determination process shown in Fig. 10. Referring to Fig. 16, in the fuzzy determination process, first, the data obtained in the preprocessing, such as the blue area ratio being 99.971, the yellow area being 0.066, the white area being 0.029, and the red area being 0.00 as described above, is input. As the first stage of fuzzy inference (S30), the first stage of deterioration determination is performed to determine whether it is a defective inspection or a measurement target (S31). If it is a measurement target ( "measurement target" in S31), the second stage of fuzzy inference is performed (S32), and the second stage of deterioration determination process is carried out (S33). Here, it is determined whether it is sound, dangerous, or requires attention. If it is determined to be sound or dangerous, the process ends.

[0077] Only those determined to require attention proceed to the third stage of fuzzy inference (S34), the second stage of deterioration determination process is carried out (S35), and it is determined to be either Attention 1 or Attention 2.

[0078] Next, an ultrasonic flaw detector to which this system is applied will be described. FIG. 17 is a diagram showing the overall configuration of an anchor bolt deterioration determination device 10 to which the above-described flowchart is applied. Referring to FIG. 17, the anchor bolt deterioration determination device 10 includes a control unit 20 including a CPU, a detection unit 21 having an ultrasonic probe connected to the control unit 20, and an application 22 included in the control unit 20. The automatic determination system for the degree of deterioration in the anchor bolt described above is included in this application. Further, this application performs from the creation of a developed view showing the degree of deterioration of the anchor bolt to the determination of the deterioration of the anchor bolt, and outputs the result.

[0079] Although embodiments of the present invention have been described with reference to the drawings, the present invention is not limited to the illustrated embodiments. Various changes can be made to the illustrated embodiments within the same range or an equivalent range of the present invention.

Industrial Applicability

[0080] According to this invention, in the anchor bolt deterioration determination device, since a worker can determine the degree of deterioration on site, it is advantageously used as an automatic determination system for the degree of deterioration in the anchor bolt.

Explanation of Reference Numerals

[0081] 10 Anchor bolt deterioration determination device, 20 Control unit, 21 Detection unit, 22 Application.

Claims

1. An automatic determination system for the degree of deterioration of an anchor bolt that automatically determines the degree of deterioration of the anchor bolt based on a developed drawing showing the degree of deterioration of the pre-made anchor bolt, The developed drawing is created based on data detected by an ultrasonic probe for the degree of deterioration of the anchor bolt, and shows the degree of deterioration of the anchor bolt, its position, and its size. In the developed drawing, the sound part of the bolt, the half-depth defect part of the thread, and the full-depth defect part of the thread are separated by a predetermined color. Perform preprocessing to read the ratio and occurrence position from the developed drawing according to the predetermined color. An automatic determination system for the degree of deterioration of an anchor bolt that automatically determines the degree of deterioration of the anchor bolt based on the result of the preprocessing.

2. The step of automatically determining the degree of deterioration of the anchor bolt is performed using fuzzy theory. The automatic determination system for the degree of deterioration of the anchor bolt according to Claim 1.

3. The degree of deterioration of the anchor bolt is separated into three levels: sound, caution, and danger. The automatic determination system for the degree of deterioration of the anchor bolt according to Claim 2.

4. The step of determining the degree of deterioration of the anchor bolt performed using fuzzy theory is divided into a first stage and a second stage. The automatic determination system for the degree of deterioration of the anchor bolt according to Claim 3.

5. In the first stage, the deterioration of sound and danger is determined, and in the second stage, the deterioration at the caution level is determined. The automatic determination system for the degree of deterioration of the anchor bolt according to Claim 4.

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