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Catenary Video Image Cloud Intelligent Monitoring and Fault Identification Method

A fault identification and video image technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as no application solutions, and achieve the effect of high accuracy of comparison and fault identification

Active Publication Date: 2020-05-22
CHINA RAILWAY FIRST SURVEY & DESIGN INST GRP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Many scholars have proposed a variety of fault identification algorithms for catenary components, but they have not formed a complete application solution

Method used

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  • Catenary Video Image Cloud Intelligent Monitoring and Fault Identification Method
  • Catenary Video Image Cloud Intelligent Monitoring and Fault Identification Method
  • Catenary Video Image Cloud Intelligent Monitoring and Fault Identification Method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0063] (1) Training deep network models for intelligent monitoring and fault identification, including catenary normal state monitoring model and catenary component fault identification model. The specific steps are as follows:

[0064] (1.1) Establish a standard data set for catenary normal state. Specifically, a camera is installed in the cab of the operating train to capture all video images of the line, and three frames of pictures are extracted for each span: extract the catenary panorama picture taken near the storage pillar according to the mileage, and store it with the catenary pillar number as the file name; The overall image of catenary taken near the pillars of each span, and two frames of pictures are extracted in each span, reflecting the status of components such as catenary cables, catenary wires and suspension strings in this span. Taking the 3689th pillar of the Zhengxi Line as an example, the file names of the three extracted pictures are ZX3689, ZX368901 a...

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PUM

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Abstract

The present invention relates to catenary video image cloud intelligent monitoring and fault identification method, through (1) model training, (2) video image acquisition and transmission, (3) video image data distributed storage, (4) intelligent monitoring and fault identification tasks Scheduling, (5) catenary condition maintenance process operation in five steps to achieve. The present invention establishes a normal state standard image library and a component abnormal image library by collecting high-speed railway catenary normal state images and component abnormal images, and then constructs catenary state comparison algorithms and catenary state comparison algorithms based on normal state standard image libraries based on deep learning technology The component fault identification algorithm provides a feasible method for catenary non-caterary monitoring.

Description

technical field [0001] The invention relates to the field of intelligent analysis of catenary faults of electrified railways, in particular to a cloud intelligent monitoring and fault identification method for catenary video images. Background technique [0002] The catenary system is an important part of the electrified railway system, which undertakes the key work of transmitting the electric energy in the traction network to the electric locomotive. Due to the complex mechanical and electrical interactions between the pantograph and the catenary device, the vibration and shock generated by the long-term operation of the train will inevitably cause the catenary support device to fail, such as the fasteners of the wrist-arm connectors loosening or even Falling off seriously affects the safe operation of the train. The catenary is exposed to wind and sun in the outdoor environment, and it is easy to be damaged (contact wire wear, insulator rupture, etc.), and foreign object...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06Q10/00
CPCG06Q10/20G06V20/41G06V20/52
Inventor 张珹张学武聂晶鑫田升平李飞靖稳峰乔琛王玉环郑刚金光张晓栋赵玮吴亚飞李晋刘刚宫衍圣隋延民
Owner CHINA RAILWAY FIRST SURVEY & DESIGN INST GRP