0

Full Content is available to subscribers

Subscribe/Learn More  >

Vision Based Fault Detection of Automated Assembly Equipment

[+] Author Affiliations
Greg Szkilnyk, Kevin Hughes, Brian Surgenor

Queen’s University, Kingston, ON, Canada

Paper No. DETC2011-48493, pp. 691-697; 7 pages
doi:10.1115/DETC2011-48493
From:
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5480-8
  • Copyright © 2011 by ASME

abstract

Machine faults and breakdowns are a concern for the manufacturing industry. Automated assembly machines typically employ many different types of sensors to monitor machine health and feedback faults to a central controller for review by a technician or engineer. This paper describes progress with a project whose goal is to examine the effectiveness of using machine vision to detect ‘visually cued’ faults in automated assembly equipment. Tests were conducted on a laboratory scale conveyor apparatus that assembles a simple 3-part component. The machine vision system consisted of several conventional webcams and image processing in LabVIEW. Preliminary results demonstrated that the machine vision system could identify faults such as part jams and feeder jams; however the overall effectiveness was limited as this technique can only detect faults known prior to creating the vision system. Future work to create a more robust system is currently underway.

Copyright © 2011 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In