Video event detection for fault monitoring in assembly automation

Authors:
Kevin Hughes;Heshan Fernando;Greg Szkilnyk;Brian Surgenor;Michael Greenspan
Affiliations:
Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada;Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON K7L 3N6, Canada;Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON K7L 3N6, Canada;Department of Mechanical and Materials Engineering, Queen's University, Kingston, ON K7L 3N6, Canada;Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2014

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Abstract

A major goal of many manufacturers is to minimise production downtime caused by machine faults and equipment breakdowns. This goal is typically achieved using sensor-based systems that can quickly detect and diagnose machine faults of various types. This paper proposes the use of a video event detection method based on spatiotemporal volumes STVs in a fault monitoring application to complement and improve upon existing systems. To detect faults, images are captured using a single camera from several different regions of an assembly machine testbed. The motion is segmented in each image creating binary frames which are stacked to build a STV. Normal operation of the machine is modelled by building a STV from several training sequences. New STVs are compared to the model and classified as either normal or faulty behaviour based on a calculated similarity measure. Test results show that the system is very effective on the data sets collected.