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Method for detecting adverse state of inclined sleeve part screws of high-speed train overhead line system

A technology of sleeve parts and bad states, applied in computer parts, measuring devices, instruments, etc., can solve the problems of complex images and difficult fault detection, and achieve high correct detection rate, simple and effective state detection, and simplified difficulty. Effect

Active Publication Date: 2017-02-01
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

Since the images of catenary support and suspension devices collected on site are generally complex, it is difficult to use image processing technology to detect the fault of the brace sleeve

Method used

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  • Method for detecting adverse state of inclined sleeve part screws of high-speed train overhead line system
  • Method for detecting adverse state of inclined sleeve part screws of high-speed train overhead line system
  • Method for detecting adverse state of inclined sleeve part screws of high-speed train overhead line system

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Embodiment Construction

[0054] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. figure 1 It is a process block diagram of the method of the present invention. Figure 2 to Figure 5 It shows the position of the brace sleeve screw in the image collected on site, highlighting that it is difficult to detect such a small part. The details are as follows:

[0055] 1. Positioning and extraction of the brace sleeve

[0056] 1) The feature operator is invariant to image scaling, rotation and brightness changes. Since cell units may be repeated between adjacent blocks, an image with a resolution of 64×64 contains 7×7 blocks. The feature vectors of all blocks in the image are connected together to obtain the HOG feature vector of the entire image, and the final HOG feature descriptor contains 1764 vectors to form dimensions.

[0057] The value of any point (x, y) in the integral map is defined as the sum of the gray value...

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Abstract

The invention discloses a method for detecting an adverse state of inclined sleeve part screws of a high-speed train overhead line system. The method comprises steps that firstly, a sample database of inclined sleeve parts is established, an AdaBoost classifier cascaded with HOG characteristic training of samples is extracted, and a supporting vector classifier is trained; secondly, Hough transformation is employed to realize extraction of an inclined sleeve inclination angle of a target image, and the inclination angle is made to rotate to a vertical direction; during fault determination, a bolt length and diameter ratio is taken as a criteria of a bolt loosing fault, and a relevant threshold is set to determine the bolt loosing fault; the bolt loosing fault is determined according to the position of a thin nut, differential processing on pixel accumulated distribution in a horizontal direction is carried out, and whether loosing occurs is determined according to a relevant horizontal pixel distribution change rate. Through the method, the state of inclined sleeve screw parts of the high-speed train overhead line system can be directly detected, an objective, true and accurate detection analysis result is acquired, and disadvantages of a traditional manual detection method are overcome.

Description

technical field [0001] The invention relates to the field of high-speed railway catenary fault detection, in particular to an image processing-based method for detecting bad states of catenary brace sleeve screws. Background technique [0002] In the L-shaped arm support device of the high-speed railway catenary, the two ears of the brace sleeve are important load-bearing parts. In order to ensure the safety of the train, the construction quality of this part has strict requirements. For jackbolt socket lugs, screws are an important fastener. The vibration or construction defects generated during the long-term operation of the train may cause the sleeve screws to loosen or fall off and other bad conditions, which will reduce the load-bearing capacity of the wrist arm, reduce the mechanical strength of the catenary, and increase the possibility of accidents. The 4C system technical specification promulgated by the former Ministry of Railways includes high-definition video mo...

Claims

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Application Information

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IPC IPC(8): G06K9/62G01D21/00
CPCG01D21/00G06F18/2411G06F18/214
Inventor 刘志刚陈隽文钟俊平韩志伟
Owner SOUTHWEST JIAOTONG UNIV
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