School of Engineering
Computer and Technology Building, CM 109
+44 (0) 1772 89 3266
Subject Areas: Mechanical, Mechatronics and Maintenance Engineering
Ahmed Onsy is a Principal Lecturer, Academic Lead for Mechanical and Maintenance Engineering area. He has been awarded his PhD from the School of Mechanical and Systems Engineering, Design Unit and Mechatronics Group, Newcastle University, UK. His main research interests are intelligent diagnostics and health management systems, intelligent maintenance systems, advanced mechatronics, and embedded systems which can be directly applied to Intelligent Diagnostic and Health Management (DHM) and Predictive Health Monitoring (PHM) systems for oil well, wind turbine, aerospace (SHM & HUM), marine, and automotive applications. Ahmed is contributing to research within the area of Maintenance and Intelligent Machines (Tribotronics) and is a member of the Jost Institute for Tribotechnology
Ahmed has 25 years’ experience in academia and R&D; during which he concluded and supervised several projects in the area of:
ONSY, A., FOUAD, M., SHAW, B. A. & Dansereau, R. M. (2014) A New Technique for Monitoring Gears Surface Failures Using Enhanced Image Registration Method International SAE International Journal Aerospace. September Volume , Issue 1.
Helmy, M., ONSY, A. HUSSEIN, W. M., & EL SHERIF, I. (2014) Development of an Advanced Diagnostic System for Automotive Mechanical Transmissions. International Journal of COMADEM. 2014, 17(2), 39- 44. ISSN 1363-7681.
HUSSEIN, W. M., ONSY, A., EL SHERIF, I. (2013) Health Monitoring of Electro-Pneumatic Controlled Systems using Multivariate Latent Methods: An Experimental Validation, SAE International Journal of Materials and Manufacturing. January 2014 Volume 7, Issue 1.
ONSY, A. (2013) A New Acoustic Emission Remote Sensing System: an Experimental Validation of Wheel Bearing Condition Monitoring. SAE International Journal Aerospace. December 2013 Volume 6, Issue 2.
ONSY, A., BICKER, R. & SHAW, B.A. (2013) Predictive Health Monitoring of Gear Surface Fatigue Failure Using Model-based Parametric Method Algorithms; An Experimental Validation. SAE International Journal Aerospace. September 2013doi:10.4271/2011-01-2700 Volume 6, Issue 1.