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Bolt Installation Defect Detection Based on a Multi-Sensor Method.

Shizhao An1, Muzheng Xiao1, Da Wang1

  • 1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-sensor approach for detecting bolt installation defects, combining visual and torque sensors. This method accurately identifies issues like missing bolts and torque errors, enhancing structural safety in automated assembly.

Keywords:
YOLO v3bolt installationdefect detectionmulti-sensor

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Area of Science:

  • Industrial Automation
  • Mechanical Engineering
  • Robotics

Background:

  • Articulated robots are increasingly used for bolt installation in industrial automation, improving efficiency but risking installation defects.
  • Bolt installation defects can compromise structural integrity and lead to safety hazards.
  • Current single-sensor detection methods (visual or torque) are insufficient for comprehensive defect identification.

Purpose of the Study:

  • To develop and validate an efficient multi-sensor method for detecting various bolt installation defects.
  • To overcome the limitations of single-sensor systems in identifying incorrect assembly, missing bolts, and torque issues.

Main Methods:

  • Utilized a trained You Only Look Once (YOLO) v3 network for image analysis from visual sensors, achieving high recognition rates.
  • Integrated torque and angle sensors to assess bolt tightness and detect slippage.
  • Combined data from visual, torque, and angle sensors for comprehensive defect detection.

Main Results:

  • The visual detection component achieved a 99.75% recognition rate with an average confidence of 0.947 at 48 FPS.
  • The multi-sensor system effectively identified complex defects such as missing bolts, incorrect torque, and bolt slippage.
  • Demonstrated superior accuracy compared to traditional single-sensor methods in experimental validation.

Conclusions:

  • A multi-sensor approach integrating visual and torque/angle sensing provides a robust solution for detecting diverse bolt installation defects.
  • This method enhances the reliability and safety of automated bolt assembly processes.
  • The developed system meets real-time detection requirements for industrial applications.