Damper fault detection method and system based on intelligent pattern recognition

Abstract

The invention relates to the field of artificial intelligence, and provides a vibration damper fault detection method and system based on intelligent pattern recognition, and the method and system employ a pattern recognition method for defect recognition, and comprise the steps: obtaining a surface gray image of a vibration damper; obtaining the uniformity of each pixel point on the surface gray level image of the shockproof hammer; obtaining a plurality of connected domains on the surface grayscale image of the shockproof hammer; calculating a reconstructed gray value of a pixel point in the connected domain to obtain a reconstructed gray image; segmenting the reconstructed grayscale image to obtain an in-contour image; if the texture exists in the image in the contour, judging that the stockbridge damper has defects; if no texture exists in the image in the contour, judging that the shockproof hammer is a qualified product; and classifying the defects of the shockproof hammer according to the texture shape in the image in the contour. Therefore, according to the damper fault detection method and system based on intelligent pattern recognition, defect detection and recognition can be achieved, and the detection efficiency is high.

Description

technical field

[0001] The invention relates to the field of artificial intelligence, in particular to a method and system for detecting a fault of an anti-vibration hammer based on intelligent graphic recognition. Background technique

[0002] Anti-vibration hammers are used in power transmission lines in order to reduce vibrations caused by external forces. The general transmission line is at high altitude and has a relatively large span. When the wire is subjected to external force, it is prone to vibration. Frequent vibration of the wire can easily cause periodic bending and fatigue, and the transmission line is broken, hardware wear and wire breakage. . The anti-vibration hammer is generally installed on both sides of the suspension point. After the anti-vibration hammer is installed, it can produce a motion opposite to the vibration phase of the wire, thereby weakening or eliminating the vibration of the wire.

[0003] The raw materials used for the components of the...

Examples

Embodiment 1

[0042] An embodiment of an anti-vibration hammer fault detection method based on intelligent graphic recognition of the present invention, such as figure 1 shown, including:

[0043] 101. Acquire a grayscale image of the surface of the anti-vibration hammer.

[0044] 102. Perform sliding window processing on the grayscale image of the shockproof hammer surface, and calculate the uniformity of the center pixel of the sliding window according to the grayscale value of each pixel in the sliding window and the grayscale mean of all pixels, and obtain the grayscale of the shockproof hammer surface. The uniformity of each pixel on the image.

[0045] Calculate the uniformity of the pixel points in the sliding window by using the gray value of the pixel point in each sliding window, and use the uniformity value as the uniformity of the center pixel point. Through this method, each pixel on the grayscale image of the shock-proof hammer surface is obtained. point uniformity.

[0046...

Embodiment 2

[0054] An embodiment of an anti-vibration hammer fault detection method based on intelligent graphic recognition of the present invention, such as figure 2 shown, including:

[0055] The scenarios for this embodiment are as follows: first, a camera is set up, a light source is arranged, a grayscale image of the surface of the anti-vibration hammer product is collected, and the obtained grayscale image of the surface of the anti-vibration hammer product is processed to obtain a uniform target area. The gray gradient is used for reconstruction, the watershed algorithm is used to segment the reconstructed image, and the image recognition technology is used to process the segmentation results to achieve the purpose of classifying defect types.

[0056] 201. Acquire a grayscale image of the surface of the anti-vibration hammer.

[0057] In this embodiment, it is necessary to identify the fault of the anti-vibration hammer in the production process, and the surface image of the an...

Embodiment 3

[0093] An embodiment of an anti-vibration hammer fault detection system based on intelligent graphic recognition of the present invention, such as image 3 shown, including:

[0094] The image acquisition unit obtains the grayscale image of the surface of the shockproof hammer;

[0095] Set the camera just above the anti-vibration hammer, place the anti-vibration hammer on the white background, collect the image of the anti-vibration hammer, segment the obtained image to obtain the image of the anti-vibration hammer without the background, and then pass the image of the anti-vibration hammer without the background. Obtain a grayscale image of the shock hammer surface.

[0096] The image processing unit performs sliding window processing on the grayscale image of the anti-vibration hammer surface, and calculates the uniformity of the center pixel of the sliding window according to the gray value of each pixel in the sliding window and the average gray value of all pixels to ob...

technical field

[0001] The invention relates to the field of artificial intelligence, in particular to a method and system for detecting a fault of an anti-vibration hammer based on intelligent graphic recognition. Background technique

[0002] Anti-vibration hammers are used in power transmission lines in order to reduce vibrations caused by external forces. The general transmission line is at high altitude and has a relatively large span. When the wire is subjected to external force, it is prone to vibration. Frequent vibration of the wire can easily cause periodic bending and fatigue, and the transmission line is broken, hardware wear and wire breakage. . The anti-vibration hammer is generally installed on both sides of the suspension point. After the anti-vibration hammer is installed, it can produce a motion opposite to the vibration phase of the wire, thereby weakening or eliminating the vibration of the wire.

[0003] The raw materials used for the components of the...

Examples

Embodiment 1

[0042] An embodiment of an anti-vibration hammer fault detection method based on intelligent graphic recognition of the present invention, such as figure 1 shown, including:

[0043] 101. Acquire a grayscale image of the surface of the anti-vibration hammer.

[0044] 102. Perform sliding window processing on the grayscale image of the shockproof hammer surface, and calculate the uniformity of the center pixel of the sliding window according to the grayscale value of each pixel in the sliding window and the grayscale mean of all pixels, and obtain the grayscale of the shockproof hammer surface. The uniformity of each pixel on the image.

[0045] Calculate the uniformity of the pixel points in the sliding window by using the gray value of the pixel point in each sliding window, and use the uniformity value as the uniformity of the center pixel point. Through this method, each pixel on the grayscale image of the shock-proof hammer surface is obtained. point uniformity.

[0046...

Embodiment 2

[0054] An embodiment of an anti-vibration hammer fault detection method based on intelligent graphic recognition of the present invention, such as figure 2 shown, including:

[0055] The scenarios for this embodiment are as follows: first, a camera is set up, a light source is arranged, a grayscale image of the surface of the anti-vibration hammer product is collected, and the obtained grayscale image of the surface of the anti-vibration hammer product is processed to obtain a uniform target area. The gray gradient is used for reconstruction, the watershed algorithm is used to segment the reconstructed image, and the image recognition technology is used to process the segmentation results to achieve the purpose of classifying defect types.

[0056] 201. Acquire a grayscale image of the surface of the anti-vibration hammer.

[0057] In this embodiment, it is necessary to identify the fault of the anti-vibration hammer in the production process, and the surface image of the an...

Embodiment 3

[0093] An embodiment of an anti-vibration hammer fault detection system based on intelligent graphic recognition of the present invention, such as image 3 shown, including:

[0094] The image acquisition unit obtains the grayscale image of the surface of the shockproof hammer;

[0095] Set the camera just above the anti-vibration hammer, place the anti-vibration hammer on the white background, collect the image of the anti-vibration hammer, segment the obtained image to obtain the image of the anti-vibration hammer without the background, and then pass the image of the anti-vibration hammer without the background. Obtain a grayscale image of the shock hammer surface.

[0096] The image processing unit performs sliding window processing on the grayscale image of the anti-vibration hammer surface, and calculates the uniformity of the center pixel of the sliding window according to the gray value of each pixel in the sliding window and the average gray value of all pixels to ob...

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