Method for identifying loss fault of assembled nut of cross beam of railway wagon

A technology for fault identification and railway wagons, applied in neural learning methods, character and pattern recognition, image data processing, etc., can solve problems such as missed detection and wrong detection of cross-beam assembly nuts, increase accuracy and improve detection efficiency , the effect of accurate classification

Active Publication Date: 2020-05-01
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
View PDF11 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Aiming at the problem of missed detection and wrong detection caused by manual inspection of images in the detection of lost faults of cross beam assembly nuts, the present invention provides a fault identification method for missing cross beam assembly nuts of railway wagons

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for identifying loss fault of assembled nut of cross beam of railway wagon
  • Method for identifying loss fault of assembled nut of cross beam of railway wagon
  • Method for identifying loss fault of assembled nut of cross beam of railway wagon

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0025] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0026] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but not as a limitation of the present invention.

[0027] A method for identifying faults at the installation position of the brake beam of a railway freight car a...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for identifying a loss fault of an assembled nut of a cross beam of a railway wagon, solves the problems of missing detection and wrong detection caused by detecting the loss fault of the assembled nut of the cross beam in an existing manual image inspection mode, and belongs to the field of fault detection of railway wagons. The method comprises the following steps: constructing a triangular hole sample set and a nut sample set; training the two sample sets by using a semantic segmentation-based U-shaped deep learning network to obtain semantic segmentation-based U-shaped deep learning network optimal weights corresponding to the two sample sets; intercepting a triangular hole and a triangular hole nut part of a to-be-detected spanning beam image, and obtaining the to-be-detected sub-image; inputting the to-be-detected sub-image into the U-shaped deep learning network to obtain a triangular hole binary image; and when a screw support is arranged at thetriangular hole and no cotter pin exists in the nut binary image, further judging whether a nut exists in the nut binary image or not, if so, giving a cross beam assembly nut loss fault alarm, and ifnot, waiting for the next to-be-detected cross beam image.

Description

technical field [0001] The invention relates to a fault identification method, in particular to a fault identification method for missing assembly nuts of cross beams of railway freight cars, and belongs to the field of fault detection of railway freight cars. Background technique [0002] The missing fault of the assembly nut of the cross beam of the railway freight car is a kind of fault that endangers the safety of the train. In the detection of the missing fault of the assembly nut of the cross beam, the fault detection is carried out by manually checking the image. Due to the fact that the inspectors are prone to fatigue and omissions during the work process, it is easy to cause missed inspections and wrong inspections. Contents of the invention [0003] Aiming at the problem of missed detection and wrong detection caused by manual inspection of images, the present invention provides a fault identification method for missing assembly nuts of railway freight cars. [...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06T7/12G06N3/08G06N3/04
CPCG06T7/0002G06T7/12G06N3/08G06N3/045G06F18/24
Inventor 邓艳
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap