Heshan is currently a PhD candidate in the Mining Systems Laboratory (MSL) at Queen’s University under the supervision of Dr. Joshua Marshall. He is interested in research and development of intelligent systems for industrial applications–machines and processes that can learn and adapt to operate reliably in dynamic and complex environments. For his Ph.D. research, Heshan has been investigating learning-based force control algorithms for robust and reliable robotic excavation of fragmented rock for underground mining. 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.


Jackson Hall, Room 112
Queen’s University
Kingston, ON K7L 3N6


External Links

TA Courses

Queen’s University

MINE 472 Mining Systems, Automation, and Control (winter 2016)
MECH 350 Automatic Controls (winter 2013 to 2016)
MECH 452 – Mechatronics (fall 2012 to 2015)


Journal Papers

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)

H. Fernando, J. A. Marshall, H. Almqvist, and J. Larsson.  Towards controlling bucket fill factor in robotic excavation by learning admittance control setpoints.  In Proceedings of the 11th Conference on Field and Service Robotics (FSR 2017), Zürich, Switzerland, September 2017.

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.

Academic Thesis

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).

Fernando, H, Alternative Wind Turbine Technologies, B.Eng (Hons) Thesis, Department of Mechanical Engineering, University of Victoria, Victoria, BC, May 2011 (advisor: Curran Crawford).