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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
  • 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


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



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