Vacuum arc-extinguishing chamber guide rod coaxiality fault diagnosis method based on X-ray imaging

Abstract

The invention discloses a vacuum arc-extinguishing chamber guide rod coaxiality fault diagnosis method based on X-ray imaging. The method comprises the following steps: acquiring a plurality of local images of a vacuum arc-extinguishing chamber by using an X-ray imaging technology; splicing the plurality of local images into a complete vacuum arc-extinguishing chamber digital ray DR image by adopting a direction template matching algorithm; a contrast limited adaptive histogram equalization algorithm is adopted to enhance the image contrast so as to highlight guide rod information; designing a cross-correlation algorithm to position the center of the guide rod by combining the axial symmetry characteristic of the guide rod; dividing the guide rod image into a plurality of sections along the vertical direction, calculating the position of a central point section by section, and fitting the central points of the plurality of sections through a least square method so as to determine the optimal symmetry axis of the guide rod; and calculating the slope difference value of the optimal symmetry axis, and if the slope difference value exceeds a preset threshold value, judging that the guide rod has an inclination fault. When the coaxiality of the guide rod is detected and analyzed, the method can effectively detect the inclination fault of the guide rod, and the fault diagnosis and detection precision of a core component can be remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of high-voltage power equipment detection, and in particular to a vacuum interrupter guide rod coaxiality fault diagnosis method based on X-ray imaging. Background Art

[0002] The vacuum interrupter is a key component of the switch of high-voltage power equipment. It uses the excellent insulation properties of the vacuum environment in the tube to effectively extinguish the arc. At the same time, the guide rod structure of the vacuum interrupter directly determines the stability of the overall structure and plays a vital role in the normal operation of the overall structure. Due to the particularity of the working environment of the vacuum interrupter, it is very important to maintain the stability of the guide rod structure. However, in actual production, due to the complexity of the assembly process and material manufacturing, problems such as improper assembly, material deformation, and structural defects may occur. If these faults are not discovered in time, they may pose a serious threat to high-voltage power equipment and personnel safety. Given that the guide rod fault of the vacuum interrupter exists inside the product and cannot be directly detected by the naked eye, it is particularly important to use appropriate non-destructive testing technology to diagnose its fault. Summary of the invention

[0003] In view of the above problems, the present invention provides a vacuum interrupter guide rod coaxiality fault diagnosis method based on X-ray imaging. The method combines image processing technology to effectively detect guide rod tilt faults and improve the accuracy of fault diagnosis.

[0004] Specifically, the present invention is implemented through the following scheme: A method for diagnosing coaxiality fault of a vacuum interrupter guide rod based on X-ray imaging, comprising: Use X-ray imaging technology to obtain multiple local images of the vacuum interrupter; Using the directional template matching algorithm, multiple local images are stitched into a complete digital ray DR image of the vacuum interrupter; The contrast-limited adaptive histogram equalization (CLAHE) algorithm is used to enhance the image contrast to highlight the guide rod information; Combined with the axisymmetric characteristics of the guide rod, a cross-correlation algorithm is designed to locate the center of the guide rod; The guide rod image is divided into multiple segments along the vertical direction, the center point position is calculated segment by segment, and the center points of multiple segments are fitted by the least square method to determine the optimal symmetry axis of the guide rod; The slope difference of the optimal symmetry axis is calculated. If it exceeds a preset threshold, it is determined that the guide rod has a tilt fault.

[0005] Preferably, the multiple local images are multiple local DR images of the vacuum interrupter chamber acquired by multi-dimensional mechanical movement using an X-ray digital imaging system.

[0006] Preferably, the directional template matching algorithm selects a template area with significant matching information and a significant area to be matched to reduce the amount of calculation, projects the two-dimensional image in the horizontal and vertical directions respectively, obtains two one-dimensional matrices after projection, and then performs one-dimensional matching based on the correlation. Calculate the correlation coefficient between the one-dimensional matrix and the registration template : ; in, For the coordinate point The correlation coefficient at , P and Q are the vertical projection one-dimensional matrices of the registration template and the registered sub-image, respectively. is the covariance of P and Q, is the variance of P, is the variance of Q; solve for the correlation coefficient The maximum value in the middle determines the vertical position of the registration template in the reference template.

[0007] Preferably, the contrast limited adaptive histogram equalization algorithm is obtained by the following formula: ; in, For local area Medium pixel value The corresponding cumulative distribution function value is is the pixel value range, is the pixel value after enhancement.

[0008] To avoid the influence of noise, the contrast-limited adaptive histogram equalization algorithm limits the contrast of the local area and recalculates the cumulative distribution function value: ; ; in, The original gray level The frequency in the local area, is the frequency after clipping, is the maximum frequency threshold, is the cumulative distribution function value after clipping.

[0009] Preferably, the cross-correlation algorithm expression is: in, represents the offset, t represents the data point, and Represents two different one-dimensional data. It indicates the correlation degree of two one-dimensional data at different offsets, and has the maximum correlation when it is at the peak.

[0010] As a preferred method, since the guide rod structure has an approximate symmetry in the horizontal direction, the images of some guide rods are averaged in the vertical direction to obtain one-dimensional data. , then Flip horizontally to get flipped one-dimensional data , and The relationship is expressed as: ; in, is the horizontal width of the image.

[0011] definition The symmetry center is the center position of the guide rod, then the following equation is obtained: ; ; Construct the cross-correlation function again: ; in About The autocorrelation function of The autocorrelation function obtains a peak value, and the coordinates of the peak value of the autocorrelation function are the offset .

[0012] As a preference, the guide rod region image is divided into image patch, The image center of a small image patch can be expressed as: ; ; ; The image is divided into Equal parts, , and Corresponding to The center point of the image, is the height of the image, is the spline curve objective function, represent the slope and intercept of the best fitting line, respectively.

[0013] As a preference, the slope of the moving guide rod area is calculated according to the above-mentioned optimal fitting straight line slope calculation formula. and the slope of the static guide rod area , set the slope difference threshold ,like , it is considered that the guide rod has a tilt fault.

[0014] Compared with the prior art, the present invention uses X-ray imaging technology to directly display the internal information of the object, avoiding the electromagnetic noise and mechanical noise interference caused by the traditional indirect measurement method, and on this basis, proposes a directional template matching algorithm, which effectively solves the problem of incomplete images caused by the limitation of the detector imaging area. By utilizing the axially symmetrical structural characteristics of the guide rod, the optimal symmetry axis of the guide rod is determined by the cross-correlation algorithm and the least squares fitting method, and the accurate diagnosis of the guide rod coaxiality fault is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A schematic flow chart of a method for diagnosing coaxial faults of a guide rod of a vacuum interrupter based on X-ray imaging provided by the present invention; Figure 2 Schematic diagram of the guide rod structure of the vacuum interrupter in the embodiment of the present invention; Figure 3 This is a representation diagram of the inclination of the guide rod of the vacuum interrupter in the embodiment of the present invention; Figure 4 Schematic diagram of guide rod image stitching in an embodiment of the present invention; Figure 5 This is a schematic diagram of guide rod image fault diagnosis in an embodiment of the present invention; Figure 6 It is a grayscale curve diagram of the vertical average data of the guide rod in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0017] The embodiment of the present invention provides a method for diagnosing coaxial faults of a vacuum interrupter guide rod based on X-ray imaging to solve the problems of low detection accuracy and complex data processing in the prior art. The scheme of the present invention is specifically implemented through the following steps, referring to Figure 1 ,include: Use X-ray imaging technology to obtain multiple local images of the vacuum interrupter; Using the directional template matching algorithm, multiple local images are stitched into a complete digital ray DR image of the vacuum interrupter; The contrast-limited adaptive histogram equalization algorithm is used to enhance the image contrast to highlight the guide rod information; Combined with the axisymmetric characteristics of the guide rod, a cross-correlation algorithm is designed to locate the center of the guide rod; The guide rod image is divided into multiple segments along the vertical direction, the center point position is calculated segment by segment, and the center points of multiple segments are fitted by the least square method to determine the optimal symmetry axis of the guide rod; The slope difference of the optimal symmetry axis is calculated. If it exceeds a preset threshold, it is determined that the guide rod has a tilt fault.

[0018] In order to make the implementation process of the solution of the present invention clear to those skilled in the art, each step is now described in detail.

[0019] The first step is to use X-ray imaging technology to obtain multiple local images of the vacuum interrupter; In this step, as an optional embodiment of the present invention, data acquisition and processing can be performed by a self-developed X-ray digital imaging system. The system includes a 225kV micro-focus X-ray source, a high-resolution amorphous silicon flat-panel detector, a 5-axis control system, and auxiliary components. The X-ray uses the XWT X-ray source of WorX GmbH, and its main parameters are shown in Table 1. The detector is Perkin Elmer XRD0822, and its key technical parameters are shown in Table 2.

[0020] Table 1 Main technical indicators of XWT X-ray source

[0021] Table 2 Main technical parameters of Perkin Elmer XRD0822 detector

[0022] Taking the TD24 vacuum interrupter as an example, the overall structure of the guide rod, the 3D model and the X-ray imaging are as follows: Figure 2 As shown, Figure 2 In the figure, a is a real picture, b is a three-dimensional model picture, and c is an X-ray imaging picture, in which the bellows 300, the moving contact 400, the static contact 500 and the guide rod (including the moving guide rod 100 and the static guide rod 200) are connected. Due to the particularity of the working environment of the vacuum interrupter and the complexity of the assembly process, the guide rod will inevitably have a tilt failure. The guide rod tilt failure is characterized by a certain amount of displacement at the connection between the moving contact 400 and the static contact 500, which causes the guide rod structure to tilt. An X-ray imaging system is used to obtain an X-ray image of the guide rod tilt for data testing, in which a typical three-dimensional model of the guide rod tilt is shown in FIG. Figure 3 As shown in a, the X-ray image is Figure 3 As shown in b.

[0023] In the second step, the directional template matching algorithm is used to stitch multiple local images into a complete digital X-ray DR image of the vacuum interrupter.

[0024] The present invention proposes a template matching algorithm based on directional projection to stitch multiple local images. Figure 4 As shown. By projecting the two-dimensional image horizontally and vertically, a one-dimensional matrix is ​​obtained, and the correlation coefficient is calculated to determine the best matching position of the template, thereby achieving accurate image stitching. Assume that the size of the reference template A is , is the width of the reference template, is the length of the reference template, and the size of the registration template B is , is the width of the registration template, is the length of the registration template. Project the registration template B vertically to obtain a length of One-dimensional matrix P of; in the reference template A, the size is taken as The sub-image of sub-images, project the sub-images vertically, and we get The length is One-dimensional matrix Q; Calculate the correlation coefficient between the one-dimensional matrix and the registration template : ; in, For the coordinate point The correlation coefficient at , P and Q are the one-dimensional matrices of the vertical projection of the registration template and the registered sub-image, respectively. is the covariance of P and Q, is the variance of P, is the variance of Q; solve for the correlation coefficient The maximum value in the middle determines the vertical position of the registration template in the reference template.

[0025] Specifically, the original image of the vacuum interrupter obtained by the X-ray imaging system is restored by wavelet image quality to obtain an image to be spliced ​​with rich details, such as Figure 4 As shown in a. Since the X-ray system can ensure that the single movement stroke is fixed, the templates selected for two adjacent images, that is, the areas to be matched, are the same. Figure 4As shown in b, the lower 1 / 4 position in the first image is selected as the matching template (template area A), and the appropriate position in the second image is selected as the search area (search area B); the second and third images are similarly operated as above (dashed area: template area C, search area D) to complete the matching operation of the images to be stitched. Since the detector scans vertically and the image resolution is fixed, only the vertical stitching operation is considered. Based on the above operation, the coordinates of the upper left corner of the matching position are determined, and the image stitching operation based on pixel coordinates is performed, as shown in Figure 4 As shown in c.

[0026] The third step is to use contrast-limited adaptive histogram equalization algorithm to enhance the image contrast to highlight the guide rod information.

[0027] Due to the similarity of the vacuum interrupter structure, the positions of the moving guide rod and the static guide rod in the image after scanning and stitching are always consistent. Therefore, in order to select the fixed position as the area of ​​interest, it is first divided into the moving guide rod area and static guide rod area ,like Figure 5 After obtaining the region of interest, it is enhanced by the contrast-limited adaptive histogram equalization algorithm, as shown in Fig. Figure 5 As shown in b. The grayscale curve of the enhanced image is as follows Figure 5 As shown in c, it can be approximated as symmetric data. Figure 5 The symmetry axis is shown in d. The contrast limited adaptive histogram equalization algorithm is expressed as: ; in, For local area Medium pixel value The corresponding cumulative distribution function value is is the pixel value range, is the pixel value after enhancement.

[0028] To avoid the influence of noise, the contrast-limited adaptive histogram equalization algorithm limits the contrast of the local area and recalculates the cumulative distribution function value: ; ; in, The original gray level The frequency in the local area, is the frequency after clipping, is the maximum frequency threshold, is the cumulative distribution function value after clipping.

[0029] Step 4: Based on the axial symmetry characteristics of the guide rod, a cross-correlation algorithm is designed to locate the center of the guide rod.

[0030] For the offset estimation of symmetrical images, the traditional image matching algorithm may be affected by the complexity of image content and noise interference, while the cross-correlation method relies on the grayscale structural characteristics of the image and has good robustness. The cross-correlation algorithm expression is: ; in, represents the offset, t represents the data point, and Represents two different one-dimensional data. It indicates the correlation degree of two one-dimensional data at different offsets, and has the maximum correlation when it is at the peak.

[0031] Since the guide rod structure has approximate symmetry in the horizontal direction, Figure 6 The image of the guide rod shown in a is averaged along the vertical direction to obtain one-dimensional data. , and its corresponding grayscale curve is as follows Figure 6 As shown in b, it has good symmetry. Then Flip horizontally to get flipped one-dimensional data , and The relationship is expressed as: ; in, is the horizontal width of the image.

[0032] definition The symmetry center is the center position of the guide rod, then the following equation is obtained: ; ; Construct the cross-correlation function again: ; in About The autocorrelation function of The autocorrelation function obtains a peak value, and the coordinates of the peak value of the autocorrelation function are the offset .

[0033] Step 5: Divide the guide rod image into multiple segments along the vertical direction, calculate the center point position of each segment, and fit the center points of multiple segments using the least squares method to determine the optimal symmetry axis of the guide rod.

[0034] The dynamic guide rod and static guide rod areas are divided into multiple image blocks along the symmetry axis, and the corresponding center points of the corresponding areas are calculated respectively, and the best fitting straight line is calculated. The guide rod fault information is judged according to the slope difference of the corresponding best fitting straight line, such as Figure 5 As shown in d. The guide rod area image is divided into image patch, The image center of a small image patch is expressed as: ; ; ; The image is divided into Equal parts, , and Corresponding to The center point of the image, is the height of the image, is the spline curve objective function, represent the slope and intercept of the best fitting line, respectively.

[0035] Step 6: Calculate the slope difference of the optimal symmetry axis. If it exceeds a preset threshold, it is determined that the guide rod has a tilt fault.

[0036] According to the slope calculation formula of the optimal fitting straight line in step 5, the slope of the dynamic guide rod area is calculated respectively. and the slope of the static guide rod area , set the slope difference threshold ,like , it is considered that the guide rod has a tilt fault.

[0037] In order to verify the effectiveness of the designed fault diagnosis method, we selected six groups of vacuum interrupter guide rod tilt fault data and a group of normal fault-free data, and calculated the corresponding slope information respectively. At the same time, in order to eliminate the influence caused by the shooting angle, a single vacuum interrupter is scanned three times at different angles. If there is slope information exceeding the fault threshold, it is judged as a fault. The inclination slopes of the dynamic and static guide rods are calculated using the method of the present invention as shown in Table 3. The slope difference is calculated. If it is greater than the established fault threshold, it is judged as a faulty structure, otherwise it is normal. In this embodiment, 6 groups of fault data and 1 group of normal data are used. The slope differences corresponding to the fault data are 0.081, 0.169, 0.081, 0.068, 0.224 and 0.126, respectively, and the slope difference corresponding to the normal non-tilted data is 0.005, which meets the threshold conditions of the guide rod tilt fault.

[0038] Table 3 Calculation of guide rod inclination slope based on cross-correlation method

[0039] The above results show that the method provided in this embodiment is used to diagnose the guide rod coaxiality fault with higher accuracy than the existing method, and can effectively detect the guide rod tilt fault with higher accuracy and reliability.

[0040] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for diagnosing the coaxiality fault of a vacuum interrupter guide rod based on X-ray imaging, characterized in that: include: Use X-ray imaging technology to obtain multiple local images of the vacuum interrupter; Using the directional template matching algorithm, multiple local images are stitched into a complete digital ray DR image of the vacuum interrupter; The contrast-limited adaptive histogram equalization algorithm is used to enhance the image contrast to highlight the guide rod information; Combined with the axisymmetric characteristics of the guide rod, a cross-correlation algorithm is designed to locate the center of the guide rod; The guide rod image is divided into multiple segments along the vertical direction, the center point position is calculated segment by segment, and the center points of multiple segments are fitted by the least square method to determine the optimal symmetry axis of the guide rod; The slope difference of the optimal symmetry axis is calculated. If it exceeds a preset threshold, it is determined that the guide rod has a tilt fault.

2. The method for diagnosing the coaxiality fault of a guide rod of a vacuum interrupter based on X-ray imaging according to claim 1, characterized in that: The multiple local images are multiple local DR images of the vacuum interrupter chamber acquired by multi-dimensional mechanical movement using an X-ray digital imaging system.

3. The method for diagnosing the coaxiality fault of a guide rod of a vacuum interrupter based on X-ray imaging according to claim 1, characterized in that: The directional template matching algorithm selects a template area with significant matching information and a significant area to be matched, projects the two-dimensional image in the horizontal and vertical directions respectively, obtains two one-dimensional matrices after projection, and then performs one-dimensional matching based on the correlation, and calculates the correlation coefficient between the one-dimensional matrix and the registration template : ； in, For the coordinate point The correlation coefficient at , P and Q are the vertical projection one-dimensional matrices of the registration template and the registered sub-image, respectively. is the covariance of P and Q, is the variance of P, is the variance of Q; solve for the correlation coefficient The maximum value in the middle determines the vertical position of the registration template in the reference template.

4. The method for diagnosing the coaxiality fault of a vacuum interrupter guide rod based on X-ray imaging according to claim 1, characterized in that: The contrast limited adaptive histogram equalization algorithm is: ； in, For local area Medium pixel value The corresponding cumulative distribution function value is is the pixel value range, is the pixel value after enhancement; To avoid the influence of noise, the contrast-limited adaptive histogram equalization algorithm limits the contrast of the local area and recalculates the cumulative distribution function value: ； ； in, The original gray level The frequency in the local area, is the frequency after clipping, is the maximum frequency threshold, is the cumulative distribution function value after clipping.

5. The method for diagnosing the coaxiality fault of a vacuum interrupter guide rod based on X-ray imaging according to claim 1, characterized in that: The cross-correlation algorithm expression is: ； in, represents the offset, t represents the data point, and Represents two different one-dimensional data; Indicates the degree of correlation between two one-dimensional data at different offsets.

6. A method for diagnosing coaxiality fault of a vacuum interrupter guide rod based on X-ray imaging according to claim 5, characterized in that: Since the guide rod structure has an approximate symmetry in the horizontal direction, some guide rod images are averaged in the vertical direction to obtain one-dimensional data. , then Flip horizontally to get flipped one-dimensional data , and The relationship is expressed as: ； in, is the horizontal width of the image; definition The symmetry center is the center position of the guide rod, then the following equation is obtained: ； ； Construct the cross-correlation function again: ； in About The autocorrelation function of The autocorrelation function obtains a peak value, and the coordinates of the peak value of the autocorrelation function are the offset .

7. A method for diagnosing coaxiality fault of a vacuum interrupter guide rod based on X-ray imaging according to claim 6, characterized in that: The guide rod area image is divided into image patch, The image center of a small image patch is expressed as: ； ； ； The image is divided into Equal parts, , and Corresponding to The center point of the image, is the height of the image, is the spline curve objective function, represent the slope and intercept of the best fitting line, respectively.

8. The method for diagnosing the coaxiality fault of a guide rod of a vacuum interrupter based on X-ray imaging according to claim 7, characterized in that: According to the slope calculation formula of the optimal fitting straight line, the slope of the moving guide rod area is calculated respectively and the slope of the static guide rod area , set the slope difference threshold ,like , it is considered that the guide rod has a tilt fault.

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