Heshan is currently a PhD candidate in the Mining Systems Laboratory (MSL) at Queen’s University under the supervision of Dr. Joshua Marshall. His research interests are in the fields of automatic control, mechatronics, robotics and machine learning. Heshan’s PhD work involves developing robust control algorithms for automated loading of fragmented rock for mining applications. Heshan completed his M.A.Sc. (2014) in the Intelligent Automation Lab (IAL), also at Queen’s University, under the supervision of Dr. Brian Surgenor. His Master’s project involved investigating the use of neural networks for fault detection and identification of automated assembly processes. Prior to that, Heshan completed his B.Eng. (2011) at The University of Victoria with a Mechatronics option.
Kingston, ON K7L 3N6
MINE 472 Mining Systems, Automation, and Control (winter 2016)
MECH 350 Automatic Controls (winter 2013 to 2016)
MECH 452 – Mechatronics (fall 2012 to 2015)
Fernando, H. and Surgenor, B., “An Unsupervised Artificial Neural Network versus a Rule-based Approach for Fault Detection and Identification in an Automated Assembly Machine,” Robotics and Computer Integrated Manufacturing, In Press, December 2015 (Manuscript No. RCM_1392).
Hughes, K., Fernando, H., Szkilnyk, G., Surgenor, B. and Greenspan, M., “Video event detection for fault monitoring in assembly automation,” Int. J. Intelligent Systems Technologies and Applications, Vol. 14, Nos. 1/2, pp. 106-113.
Conference Papers (Fully Refereed)
Fernando, H. and Surgenor, B., “An artificial neural network based on adaptive resonance theory for fault classification on an automated assembly machine,” Flexible Automation and Intelligent Manufacturing, San Antonio, Texas, 20–23 May, 2014.
Fernando, H., Chauhan, V., and Surgenor, B., “Image-based versus signal-based sensors for machine fault detection and isolation,” Proceedings from the 12th Biennial Conference on Engineering Systems Design and Analysis, Copenhagen, Denmark, 25-27 June, 2014.
Fernando, H., and Surgenor, B., “An RFID-based Automated Warehouse Project for a Course in Mechatronics,” Proceedings of the 2nd International Conference on Mechanical Engineering and Mechatronics, Toronto, 8-9 August, 2013.
Fernando, H., Hughes, K., Szkilnyk, G., Surgenor, B. and Greenspan, M. “Video event fault detection with STVs: application to a high speed assembly machine,” 41st North American Manufacturing Research Conference, Madison, 10-14 June, 2013.
Szkilnyk, G., Hughes, K., Fernando, H., and Surgenor, B., “Spatiotemporal volume video event detection for fault monitoring in assembly automation,” Mechatronics and Machine Vision in Practice (M2VIP), 19th International Conference on, pp. 20-25, 28-30 November, 2012.
Fernando, H., Siriwardana, J., and Halgamuge, S., “Can a data center heat-flow model be scaled down?” Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference on, pp.273-278, 27-29 September, 2012.
Conference Papers (Abstract Refereed)
Fernando, H., Lounsbury, C., and Surgenor, B., “Integrating RFID technology into a course in mechatronics,” Int. Conf. on Engineering Education and Research, Hamilton, Ontario, 24-26 August, 2014.
Chauhan, V., Fernando, H., and Surgenor, B., “Effect of illumination techniques for machine vision inspection for automated assembly machines,” CSME International Congress, Toronto, Ontario, 1-4 June, 2014.
Conference Presentations (Non-Author)
Waldie, J., Surgenor, B., and Dehghan, B., “Fuzzy PID and contour tracking as applied to position control of a pneumatic gantry robot,” ASME/BATH Symposium on Fluid Power & Motion Control, Sarasota, Florida, 6 – 9 October, 2013.
Fernando, H., Artificial Neural Networks for Fault Detection and Identification on an Automated Assembly Machine, M.ASc. Thesis, Department of Mechanical Engineering, Queen’s University, Kingston, ON, May 2014 (advisor: Brian Surgenor).