Method for diagnosing coaxiality fault of guide rod in vacuum interrupter based on X-ray imaging

Abstract

The present invention discloses a method for diagnosing the coaxiality fault of the guide rod in a vacuum interrupter based on X-ray imaging, which includes: obtaining multiple local images of the vacuum interrupter by using X-ray imaging technology; adopting a direction template matching algorithm to splice the multiple local images into a complete digital radiography (DR) image of the vacuum interrupter; adopting a contrast-limited adaptive histogram equalization algorithm to enhance the image contrast so as to highlight the guide rod information; designing a cross-correlation algorithm to locate the center of the guide rod in combination with the axisymmetric characteristics of the guide rod; dividing the guide rod image into multiple segments along the vertical direction, calculating the center point positions of each segment one by one, and fitting the center points of multiple segments by the least square method to determine the optimal axis of symmetry of the guide rod; calculating the slope difference of the optimal axis of symmetry, and if it exceeds a preset threshold, it is determined that the guide rod has an inclination fault. When detecting and analyzing the coaxiality of the guide rod, the method of the present invention can effectively detect the inclination fault of the guide rod and can significantly improve the fault diagnosis and detection accuracy of the core components.

Description

Technical Field

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

[0002] The vacuum interrupter is a key component of the high - voltage power equipment switch. It uses the excellent insulation characteristics of the vacuum environment inside 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 crucial role in the normal operation of the overall structure. Due to the special working environment of the vacuum interrupter, it is crucial 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 detected in time, they may pose a serious threat to the high - voltage power equipment and personnel safety. Given that the guide rod faults of the vacuum interrupter exist inside the product and cannot be directly detected by the naked eye, it is particularly important to use appropriate non - destructive testing techniques for fault diagnosis. Summary of the Invention

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

[0004] Specifically, the present invention is realized through the following scheme:

[0005] A method for diagnosing the coaxiality fault of the guide rod of a vacuum interrupter based on X - ray imaging, comprising:

[0006] Using X - ray imaging technology to obtain multiple local images of the vacuum interrupter;

[0007] Adopting a direction template matching algorithm to splice the multiple local images into a complete digital radiography (DR) image of the vacuum interrupter;

[0008] Adopting a contrast - limited adaptive histogram equalization (CLAHE) algorithm to enhance the image contrast to highlight the guide rod information;

[0009] Combining the axial symmetry characteristics of the guide rod, designing a cross - correlation algorithm to locate the center of the guide rod;

[0010] Dividing the guide rod image into multiple segments along the vertical direction, calculating the center point position of each segment one by one, and fitting the center points of multiple segments by the least - squares method to determine the optimal symmetry axis of the guide rod;

[0011] Calculating the slope difference of the optimal symmetry axis. If it exceeds the preset threshold, it is determined that the guide rod has an inclination fault.

[0012] Preferably, the multiple local images are multiple local DR images of the vacuum interrupter obtained by a multi-dimensional mechanical movement using an X-ray digital imaging system.

[0013] Preferably, the direction template matching algorithm selects the template region with significant matching information and the significant region to be matched to reduce the calculation amount. The two-dimensional image is projected in the horizontal and vertical directions respectively, and two one-dimensional matrices are obtained after projection. Then, one-dimensional matching is performed according to the correlation. Calculate the correlation coefficient between the one-dimensional matrix and the registration template :

[0014] ;

[0015] Wherein, is the correlation coefficient at the coordinate point , P and Q are the one-dimensional matrices of the vertical direction projections of the registration template and the sub-image after registration respectively, is the covariance of P and Q, is the variance of P, is the variance of Q; Solve the maximum value in the correlation coefficient to determine the vertical position of the registration template in the reference template.

[0016] Preferably, the contrast-limited adaptive histogram equalization algorithm is obtained by the following formula:

[0017] ;

[0018] Wherein, is the cumulative distribution function value corresponding to the pixel value in the local region , is the pixel value range, is the enhanced pixel value.

[0019] To avoid the influence of noise, the contrast-limited adaptive histogram equalization algorithm performs contrast limitation on the local region and recalculates the cumulative distribution function value:

[0020] ;

[0021] ;

[0022] Wherein, is the frequency of the original gray level in the local region, is the frequency after clipping, is the maximum frequency threshold, is the cumulative distribution function value after clipping.

[0023] Preferably, the expression of the cross-correlation algorithm is:

[0024]

[0025] Among them, represents the offset, t represents the data point, and represent two different one-dimensional data. represents the correlation degree of the two one-dimensional data at different offsets, and it has the maximum correlation when it is at the peak.

[0026] Preferably, due to the approximate symmetry of the guide rod structure in the horizontal direction, the one-dimensional data is obtained by taking the average of part of the guide rod image in the vertical direction , and then is horizontally flipped to obtain the flipped one-dimensional data , and The relationship is expressed as:

[0027] ;

[0028] Among them, is the width of the image in the horizontal direction.

[0029] Define The symmetry center of as the center position of the guide rod, then there is the following equation:

[0030] ;

[0031] ;

[0032] Construct the cross-correlation function again:

[0033] ;

[0034] Among them is the autocorrelation function about . According to the properties of the autocorrelation function, when the autocorrelation function reaches the peak, and the peak coordinate of the autocorrelation function is the offset .

[0035] Preferably, the guide rod region image is divided into image patches in the vertical direction. The image center of the th image patch can be expressed as:

[0036] ;

[0037] ;

[0038] ;

[0039] Among them, the image is divided into equal parts, , and respectively correspond to the center points of the th image, is the height of the image, is the spline curve objective function, respectively represent the slope and intercept of the optimal fitting straight line.

[0040] Preferably, according to the slope calculation formula of the above optimal fitting straight line, the slope of the moving guide rod area and the slope of the static guide rod area are respectively calculated, a slope difference threshold is set. If , it is considered that the guide rod has an inclination fault.

[0041] Compared with the prior art, the present invention uses X-ray imaging technology to directly display the internal information of the object, avoiding the interference of electromagnetic noise and mechanical noise caused by traditional indirect measurement methods. On this basis, a direction template matching algorithm is proposed, effectively solving the problem of incomplete images caused by the limited imaging area of the detector. Utilizing the axisymmetric structural characteristics of the guide rod, the optimal axis of symmetry of the guide rod is determined by cross-correlation algorithm and least squares fitting, realizing the precise diagnosis of the coaxiality fault of the guide rod. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The 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 to the present invention. In the drawings:

[0043] Figure 1 is a schematic flow chart of the method for diagnosing the coaxial fault of the guide rod in a vacuum interrupter based on X-ray imaging provided by the present invention;

[0044] Figure 2 is a schematic structural diagram of the guide rod in a vacuum interrupter in an embodiment of the present invention;

[0045] Figure 3 is a schematic diagram of the inclination characterization form of the guide rod in a vacuum interrupter in an embodiment of the present invention;

[0046] Figure 4 is a schematic diagram of the image stitching of the guide rod in an embodiment of the present invention;

[0047] Figure 5 is a schematic diagram of the fault diagnosis of the guide rod image in an embodiment of the present invention;

[0048] Figure 6This is the vertical average data grayscale curve graph of the guide rod in the embodiment of the present invention. Detailed implementation manners

[0049] 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 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 so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0050] The embodiment of the present invention provides a method for diagnosing the coaxial fault of the guide rod of a vacuum interrupter based on X-ray imaging to solve the problems of low detection accuracy and complex data processing in the prior art. The solution of the present invention is specifically implemented through the following steps, referring to Figure 1 , including:

[0051] Utilize X-ray imaging technology to obtain multiple local images of the vacuum interrupter;

[0052] Adopt the direction template matching algorithm to splice multiple local images into a complete digital radiography (DR) image of the vacuum interrupter;

[0053] Adopt the contrast-limited adaptive histogram equalization algorithm to enhance the image contrast to highlight the guide rod information;

[0054] Combine the axisymmetric characteristics of the guide rod to design a cross-correlation algorithm to locate the center of the guide rod;

[0055] Divide the guide rod image into multiple segments along the vertical direction, calculate the center point position for each segment, and fit the center points of multiple segments by the least squares method to determine the optimal axis of symmetry of the guide rod;

[0056] Calculate the slope difference of the optimal axis of symmetry. If it exceeds the preset threshold, it is determined that the guide rod has an inclination fault.

[0057] To make those skilled in the art clearly understand the implementation process of the solution of the present invention, the following will explain each step in detail.

[0058] The first step: Utilize X-ray imaging technology to obtain multiple local images of the vacuum interrupter;

[0059] In this step, as an optional embodiment of the present invention, data acquisition and processing can be performed through a self-developed X-ray digital imaging system. The system includes a 225 kV microfocus 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 type 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.

[0060] Table 1 Main Technical Specifications of XWT-type X-ray Source

[0061]

[0062] Table 2 Main Technical Parameters of Perkin Elmer XRD0822 Detector

[0063]

[0064] Taking the TD24 type vacuum interrupter as an example, the overall structure, 3D model and X-ray imaging of the guide rod are as Figure 2 shown Figure 2 In the figure, a is the physical drawing, b is the 3D model drawing, and c is the X-ray imaging drawing, where the bellows 300, moving contact 400, static contact 500 are connected to the guide rod (including the moving guide rod 100 and the static guide rod 200). Due to the particularity of the working environment of the vacuum interrupter and the complexity of the assembly process, the guide rod inevitably has an inclination fault. The characterization form of the guide rod inclination fault is that a certain amount of displacement occurs at the connection between the moving contact 400 and the static contact 500, and then the structure of the guide rod is inclined. The X-ray imaging system is used to obtain the X-ray image of the guide rod inclination for data testing, and the typical 3D model of the guide rod inclination is as Figure 3 shown in a in the figure, and the X-ray image is as Figure 3 shown in b in the figure

[0065] Second, adopt the direction template matching algorithm to splice multiple local images into a complete digital radiography (DR) image of the vacuum interrupter

[0066] The present invention proposes a template matching algorithm based on direction projection to splice multiple local images, as Figure 4 shown. By projecting the two-dimensional image in the horizontal and vertical directions respectively, a one-dimensional matrix is obtained, and the correlation coefficient is calculated to determine the best matching position of the template, so as to achieve the precise splicing of the image. Let the size of the reference template A be , is the width of the reference template, is the length of the reference template, 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 in the vertical direction to obtain a one-dimensional matrix P with a length of ; sequentially take sub-images with a size of in the reference template A, and at the same time sub-images can be obtained. Project the sub-images in the vertical direction to obtain one-dimensional matrices Q with a length of ; calculate the correlation coefficient of the one-dimensional matrix and the registration template :

[0067] ;

[0068] Among them, is the correlation coefficient at the coordinate point . P and Q are respectively the one-dimensional matrices of the vertical projections of the registration template and the sub-image after registration, is the covariance of P and Q, is the variance of P, is the variance of Q; Solve the maximum value in the correlation coefficient to determine the vertical position of the registration template in the reference template.

[0069] Specifically, perform wavelet image quality restoration on the original image of the vacuum interrupter obtained by the X-ray imaging system to obtain the image to be stitched with rich details, as shown in Figure 4 a. Since the X-ray system can ensure that the single movement stroke is fixed, the templates selected for adjacent two images, that is, the regions to be matched, are the same. As shown in Figure 4 b, select the lower 1 / 4 position in the first image as the matching template (template region A), and at the same time select an appropriate position in the second image as the search region (search region B); Similarly, perform the above operations on the second image and the third image (dashed region: template region C, search region D) to complete the matching operation of the images to be stitched. Since the detector scans in the vertical direction and the image resolution is fixed, only consider the vertical stitching operation. Based on the above operations, determine the upper left corner coordinates of the matching position and perform the image stitching operation based on pixel coordinates, as shown in Figure 4 c.

[0070] Step 3: Use the contrast-limited adaptive histogram equalization algorithm to enhance the image contrast to highlight the guide rod information.

[0071] Due to the similarity of the vacuum interrupter structure, the positions of the moving guide rod and the static guide rod in the scanned and stitched image always remain the same. Therefore, in order to select a fixed position as the region of interest, first divide it into the moving guide rod region and the static guide rod region , as shown in Figure 5 a. After obtaining the region of interest, perform contrast-limited adaptive histogram equalization algorithm enhancement on it, as shown in Figure 5 b. The gray curve of the enhanced image is as shown in Figure 5 c, which can be approximated as symmetric data, Figure 5 d shows the axis of symmetry. The expression of the contrast-limited adaptive histogram equalization algorithm is:

[0072] ;

[0073] Among them, For a local area The pixel value Corresponds to the cumulative distribution function value Is the pixel value range Is the pixel value after enhancement

[0074] To avoid the influence of noise, the contrast-limited adaptive histogram equalization algorithm performs contrast limitation on the local area and recalculates the cumulative distribution function value:

[0075] ;

[0076] ;

[0077] Among them Is 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

[0078] Step 4: Combine the axisymmetric characteristics of the guide rod and design a cross-correlation algorithm to locate the center of the guide rod

[0079] For the offset estimation of symmetric images, traditional image matching algorithms may be affected by the complexity of image content and noise interference, while the cross-correlation method depends on the gray-scale structure features of the image and has good robustness. The expression of the cross-correlation algorithm is:

[0080] ;

[0081] Among them Represents the offset, t represents the data point And Represent two different one-dimensional data Represents the correlation degree of two one-dimensional data at different offsets, and it has the maximum correlation when it is at the peak

[0082] Due to the approximate symmetry of the guide rod structure in the horizontal direction, take the average of the partial guide rod image shown in a in Figure 6 Vertically to obtain one-dimensional data , and its corresponding gray curve is as shown in b in Figure 6 , which has good symmetry. Then flip Horizontally to obtain the flipped one-dimensional data , And The relationship is expressed as:

[0083] ;

[0084] Among them, is the horizontal width of the image.

[0085] Define the center of symmetry as the center position of the guide rod, then there are the following equations:

[0086] ;

[0087] ;

[0088] Construct the cross-correlation function again:

[0089] ;

[0090] Among them is the autocorrelation function with respect to . According to the properties of the autocorrelation function, when the autocorrelation function reaches its peak, and the peak coordinates of the autocorrelation function are the offset .

[0091] Step 5: Divide the guide rod image into multiple segments vertically, calculate the center point position of each segment one by one, and fit the center points of multiple segments by the least squares method to determine the optimal symmetry axis of the guide rod.

[0092] Divide the moving guide rod and static guide rod regions into multiple small image blocks along the symmetry axis direction respectively, calculate the corresponding center points of the corresponding regions, and calculate the optimal fitting straight line. Judge the guide rod fault information according to the slope difference of the corresponding optimal fitting straight lines, as shown in Figure 5 d. Divide the guide rod region image into small image blocks vertically, and the image center of the th small image block is expressed as:

[0093] ;

[0094] ;

[0095] ;

[0096] Among them, the image is divided into equal parts, , and correspond to the center points of the rd image respectively, is the height of the image, is the spline curve objective function, respectively represent the slope and intercept of the optimal fitting straight line.

[0097] Step 6: Calculate the slope difference of the optimal axis of symmetry. If it exceeds the preset threshold, it is determined that there is an inclination fault in the guide rod.

[0098] Calculate the slope of the moving guide rod area and the slope of the static guide rod area respectively according to the slope calculation formula of the optimal fitting line in Step 5. and Set the slope difference threshold If it is considered that there is an inclination fault in the guide rod.

[0099] To verify the effectiveness of the designed fault diagnosis method, a total of six groups of vacuum interrupter guide rod inclination fault data and one group of normal fault-free data were selected, and the corresponding slope information was calculated respectively. At the same time, to eliminate the influence caused by the shooting angle, a single vacuum interrupter was scanned three times at different angles. If there is slope information exceeding the fault threshold, it is determined as a fault. The calculated slopes of the moving and static guide rods using the method of the present invention are shown in Table 3. Calculate the slope difference. If it is greater than the established fault threshold, it is determined as a faulty structure; otherwise, it is normal. In this embodiment, 6 groups of fault data and 1 group of normal data were 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-inclined data is 0.005, all of which meet the threshold conditions of the guide rod inclination fault.

[0100] Table 3 Calculation of Guide Rod Inclination Slope Based on Cross-Correlation Method

[0101]

[0102] The above results show that the method provided in this embodiment for diagnosing the coaxiality fault of the guide rod has higher accuracy than the existing methods, and can effectively detect the inclination fault of the guide rod, with high accuracy and reliability.

[0103] 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 equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for diagnosing the coaxiality fault of the guide rod of a vacuum interrupter based on X-ray imaging, characterized in that, Including: Obtaining multiple local images of the vacuum interrupter by using X-ray imaging technology; Adopting a directional template matching algorithm to splice the multiple local images into a complete digital radiography (DR) image of the vacuum interrupter; Adopting a contrast-limited adaptive histogram equalization algorithm to enhance the image contrast to highlight the information of the guide rod; Combining the axial symmetry characteristic of the guide rod to design a cross-correlation algorithm to locate the center of the guide rod; Dividing the guide rod image into multiple segments along the vertical direction, calculating the position of the center point for each segment, and fitting the center points of multiple segments by the least square method to determine the optimal axis of symmetry of the guide rod; Calculating the slope difference of the optimal axis of symmetry, and if it exceeds a preset threshold, determining that the guide rod has an inclination fault.

2. A method for diagnosing the coaxiality fault of the 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 obtained by using an X-ray digital imaging system through multi-dimensional mechanical movement.

3. A method for diagnosing the coaxiality fault of the guide rod of a vacuum interrupter based on X-ray imaging according to claim 1, characterized in that, The direction template matching algorithm selects a template region with significant matching information and a significant region 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 according to the correlation to calculate the correlation coefficient between the one-dimensional matrix and the registration template. : ； Among them, is the correlation coefficient at the coordinate point . P and Q are respectively the one-dimensional matrices of the vertical projections of the registration template and the sub-image after registration, is the covariance of P and Q, is the variance of P, is the variance of Q; Solve for the maximum value in the correlation coefficient to determine the vertical position of the registration template in the reference template.

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

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

6. A method for diagnosing the coaxiality fault of the guide rod of a vacuum interrupter based on X-ray imaging according to claim 5, characterized in that, Due to the approximate symmetry of the guide rod structure in the horizontal direction, one-dimensional data is obtained by taking the average of partial guide rod images along the vertical direction , and then it is horizontally flipped to obtain the flipped one-dimensional data , and The relationship is expressed as: ； Among them, is the width of the image in the horizontal direction; Definition Symmetric center is the center position of the guide bar, then the following equation holds: ； ； Constructing the cross-correlation function again: ； Among them is the autocorrelation function of . According to the properties of the autocorrelation function, when , the autocorrelation function reaches its peak value, and the peak coordinate of the autocorrelation function is the offset .

7. A method for diagnosing the coaxiality fault of the guide rod of a vacuum interrupter based on X-ray imaging according to claim 6, characterized in that, Divide the guide bar area image vertically into image patches. The image center of the th image patch is denoted as: ； ； ； Among them, the image is divided into equal parts, , and respectively correspond to the center points of the th image, is the height of the image, is the spline curve objective function, respectively represent the slope and intercept of the optimal fitting line.

8. A method for diagnosing the coaxiality fault of the guide rod of a vacuum interrupter based on X-ray imaging according to claim 7, characterized in that, The slopes of the moving guide bar area and the static guide bar area are calculated respectively according to the slope calculation formula of the optimal fitting straight line and the slope of the static guide bar area , a slope difference threshold is set. If , it is considered that there is an inclination fault in the guide bar

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