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Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
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Pages: 271-276

Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems

Hasan Yetiş •
Mehmet Karaköse
Publication DateDecember 1, 2016
Volume / IssueVol. , No. (pp. 271-276)
subject

Abstract

Continuity of production is a highly important in the days that manufacturing is becoming bigger and serial. The mistakes done while producing process cause fail on products and it may bring about even big losses for the facility. Furthermore, hitches on robots at production line may also cause crucial damages that may give rise to high repair costs and discontinuance of production. In this study, it is aimed to obtain alive bird's eye view map of production lines, which are big and impossible to be monitored with only one camera, by using multi cameras and stitching algorithms. Finding the similar scenes of input images, estimation of homography, warping and blending operations, which are the steps used in feature based image-stitching algorithms, are applied respectively on images that are taken by cameras. The assignment of second nearest neighbor distance rate adaptively makes the results more qualified. After obtaining single stitched image movement detection is actualized by using the difference of sequential frames, and anomaly movements are determined. As a result, the robots at the long production lines can be monitored in one screen, and with processing the obtained image, faults on robots that may cause damage at non-cheap machines can be handled before time.
Keywords:Condition monitoringMulti robotsProduction linesImage mosaicingImage processingFault Diagnosis
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How to Cite This Article

H. Y., M. K. (2016). Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems. International Journal of Applied Methods in Electronics and Computers, 271-276. https://doi.org/10.18100/ijamec.270410