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Fault detection method for lug fracture of high-speed railway catenary support device based on hog feature

A technology of broken ear pieces and supporting devices, which is applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problems of failure detection of parts and low degree of automation, achieve objective detection results, reduce workload, The effect of improving detection efficiency

Inactive Publication Date: 2017-08-08
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

However, the degree of automation of existing non-contact detection devices is generally not high, and the fault detection of many parts cannot be realized

Method used

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  • Fault detection method for lug fracture of high-speed railway catenary support device based on hog feature
  • Fault detection method for lug fracture of high-speed railway catenary support device based on hog feature
  • Fault detection method for lug fracture of high-speed railway catenary support device based on hog feature

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Embodiment

[0052] Training samples are manually intercepted from the catenary support and suspension device images collected earlier. Among them, the positive sample contains the rotated ears, and the rotated ears occupy the main body position in the middle of the image, figure 1 (a) shown. Negative samples randomly contain other catenary components unrelated to the rotating binaural, figure 1 (b) shown. In order to reduce the difference in HOG features caused by "alignment problems", the aspect ratio of positive and negative samples is fixed at 2:1 when intercepting, and the size is normalized to 128×64 pixels (the size of the detection window).

[0053] Extract HOG features for positive and negative samples: first divide the image into several square cells of the same size. Then merge every four adjacent cells into a square block, and the blocks can overlap each other. Use (1)-(4) to calculate the gradient magnitude (m(x,y)) and direction (θ(x,y)) of each pixel, and calculate the g...

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Abstract

The invention discloses a HOG feature-based method for detecting the breakage of lugs of a catenary support device for high-speed railways, which detects the breakage of the lugs of rotating double ears. It includes the following steps: first, establish a library of positive and negative samples for rotating both ears; then extract the HOG features of the positive and negative samples to generate feature descriptors of the samples; then, based on the AdaBoost algorithm, train the Cascade cascade classifier, and use the trained The classifier classifies and recognizes the area where the rotated ears are located and the area where the non-rotated ears are located in the image, and completes the positioning of the rotated ears in the image. Finally, the two-dimensional Gabor wavelet transform is used to screen the edge information in the rotated binaural sub-image, and then the fault crack caused by the broken ear piece is identified. The method of the invention can accurately identify the lugs with broken faults in the complex catenary suspension device image, and can greatly improve the detection efficiency compared with the manual screening method.

Description

technical field [0001] The invention relates to the technical fields of HOG feature extraction, Cascade cascade classifier training, two-dimensional Gabor wavelet transformation, edge information screening, and ear piece fracture fault identification. Background technique [0002] The rotating lugs are located at the connection of the locator, which is an important load-bearing component in the support structure of the high-speed railway catenary, and plays a vital role in the safe operation of the train. In the actual operation of the railway, the lugs are often broken due to the vibration of the train, resulting in a reduction in the structural strength of the catenary support device, and even the risk of the positioner falling off in severe cases. Therefore, it is necessary to detect the rotating binaural parts, find and replace faulty parts in time. [0003] For a long time, the detection of bad working conditions of catenary components has mainly relied on the method o...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06F18/2451
Inventor 高仕斌刘志刚韩烨钟俊平刘文强张桂南
Owner SOUTHWEST JIAOTONG UNIV
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