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Method for detecting breakage of lugs of high-speed rail contact net suspension device

A technology of a suspension device and a detection method, which is applied in the directions of optical testing flaws/defects, image data processing, instruments, etc., can solve the problem that the detection method for rotating binaural faults has not yet appeared.

Inactive Publication Date: 2013-03-20
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

However, no fault detection method for rotating binaural

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  • Method for detecting breakage of lugs of high-speed rail contact net suspension device
  • Method for detecting breakage of lugs of high-speed rail contact net suspension device
  • Method for detecting breakage of lugs of high-speed rail contact net suspension device

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Embodiment Construction

[0037] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0038] Use SIFT algorithm to figure 2 The image of the catenary support and suspension device shown and image 3 The illustrated rotating binaural template image performs local feature point matching. The matching result is like image 3 Shown. The successfully matched feature points are mainly concentrated in 3 regions, forming 3 clusters, such as Figure 4 a. Figure 4 b, and Figure 4 c is shown. The large image in the left half of the figure is the image of the catenary support and suspension device to be analyzed, and the small image in the upper right corner is the image of the rotating binaural template. The turquoise line indicates the matching of local feature points in the two images.

[0039] In turn Figure 4 a. Figure 4 b, and Figure 4 The clustering in c uses the improved RANSAC algorithm to eliminate mismatches, Figure 4 b and ...

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Abstract

The invention discloses a detection method adopting SIFT (Scale-Invariant Feature Transform) to detect a breakage fault of rotary double lugs. The method comprises the following steps of: firstly, carrying out local feature point matching on the image of a contact net supporting and suspending device and the image of a rotary double-lug template by utilizing an SIFT algorithm; secondly, screening successfully matched feature points by utilizing an improved RANSAC (Random Sample Consensus) algorithm and positioning the rotary double lugs; thirdly, extracting an upper boundary curve of the rotary double lugs by utilizing a boundary tracing method, and drawing a curvature curve of all points on the curve; and finally, carrying out difference treatment on the drawn curvature curve and the curvature curve under a normal condition at the corresponding point to judge whether the lugs have a breakage fault or not. The method provided by the invention can accurately identity the lug with the breakage fault in the image of a complex contact net suspending device, and greatly improves detection efficiency in comparison with an artificial screening method.

Description

Technical field [0001] The invention relates to the field of high-speed railway image detection, in particular to methods for SIFT (Scale-Invariant Feature Transform) image invariant feature extraction, feature point matching and screening, and ear piece fracture fault identification. Background technique [0002] The rotating ears are located at the connection of the catenary support locator of the high-speed rail. They are an important load-bearing component in the catenary support structure and play a vital role in the safe operation of the train. In the actual operation of the railway, the failure of the ear pieces is often caused by the train vibration, which causes the structural strength of the catenary support device to decrease, and in severe cases, the locator may even fall off. Therefore, it is necessary to detect the rotating binaural parts, find and replace the faulty parts in time. [0003] At present, my country's railway department mainly adopts manual methods to i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/88G06T7/00
Inventor 刘志刚韩烨张桂南韩志伟
Owner SOUTHWEST JIAOTONG UNIV
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