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Related Concept Videos

Steel Fastening Techniques01:17

Steel Fastening Techniques

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Steel sections can be joined together through various fastening techniques including riveting, bolting, and welding, each suitable for different structural requirements and conditions.
Rivets are cylindrical steel fasteners with a specially designed head. During application, rivets are heated until white-hot and then inserted through pre-drilled holes in the steel sections. A pneumatic hammer is used to shape the exposed end into a second head, securing the sections together.
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A Rail Fastener Tightness Detection Approach Using Multi-source Visual Sensor.

Qiang Han1, Shengchun Wang2, Yue Fang2

  • 1School of Science, Beijing Jiaotong University; Beijing 100044, China.

Sensors (Basel, Switzerland)
|March 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a 3D vision system for detecting rail track fastener issues. The new method accurately identifies loose or overtightened fasteners, enhancing railway safety.

Keywords:
image segmentationmulti-source visual dataobject detectionrail fastenerrailway defect detectionstructured light sensortightness detection

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Area of Science:

  • * Railway Engineering
  • * Computer Vision
  • * Metrology

Background:

  • * Traditional 2D image recognition struggles to detect critical fastener conditions like overtightening or loosening due to vibration.
  • * These conditions can lead to fastener failure and compromise railway safety.
  • * 3D visual detection technology offers a promising solution for enhanced inspection.

Purpose of the Study:

  • * To develop a multi-source visual data detection method for accurate fastener analysis.
  • * To create a robust algorithm for locating fasteners and segmenting nuts/bolts.
  • * To detect hidden dangers in railway transportation caused by abnormal fastener states.

Main Methods:

  • * Combined 2D intensity and 3D depth information from line structured light projection.
  • * Utilized dynamic template matching for fastener localization from 2D images (99.4% accuracy).
  • * Employed watershed algorithm for nut/bolt segmentation from depth images and analyzed 3D shape.

Main Results:

  • * Achieved static measurement accuracy of 0.1 mm and dynamic accuracy of 0.5 mm in the vertical direction.
  • * Successfully located fasteners with high accuracy in a dynamic train environment.
  • * Demonstrated the ability to determine if nut/bolt height meets safety thresholds.

Conclusions:

  • * The proposed 3D visual detection method effectively identifies abnormal fastener conditions.
  • * This technology enhances railway safety by detecting potentially dangerous fastener states.
  • * The system provides accurate measurements in both static and dynamic railway environments.