Automated assembly machines operate continuously to achieve high production rates. Continuous operation increases the potential for faults, with subsequent machine downtime. Early fault detection can reduce the amount of downtime. Traditional fault detection methods check for deviations from fixed threshold limits with multiple mechanical, optical and proximity sensors. The goal of this thesis was to develop and validate a machine vision inspection (MVI) system to detect and classify multiple faults using a single camera as a sensor. An industrial automated O-ring assembly machine that places O-rings on to continuously moving plastic carriers at a rate of over 100 assemblies per minute was modified to serve as the test apparatus. An Firstly, I would like express my sincere gratitude to my supervisor Dr. Brian W. Surgenor for his supervision of this project. He has provided to me an opportunity to learn both technical and professional aspects of engineering. I am very thankful to him for his encouragement, support and constant feedback throughout the course of my studies at Queen's University. He is a great teacher, supervisor and a nice person. Thank you Dr. Surgenor.
A mobile robot being used for an undergraduate course in mechatronics engineering was converted from a conventional lead acid battery to a hydrogen fuelled PEM micro fuel cell as the power source. The design of the fuel cell based power supply system is outlined. Test results of system performance are given.Implications with respect to implementation issues for micro fuel cells are briefly discussed.
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