Dr Ramzi Fayad
BSc, MSc, PhD
Research Associate (Natural Environment Research Council)
Full contact details
Sheffield University Management School
Ramzi Fayad was born on 28 April 1978 in Koura Lebanon and is a Research Associate at Sheffield University Management School.
He earned a BSc and an MSc in Computer Engineering from Balamand University, Lebanon, and completed a PhD in Manufacturing Engineering and Operations Management at the University of Nottingham, United Kingdom.
He worked as a Quality Engineer for Rolls Royce from 2000 to 2006 where he designed a fully automated monitoring system to detect non-conformance in manufacturing processes.
He worked as an Assistant Professor in Industrial Engineering and Engineering Management from 2011 until 2020.
He has published journal and conference papers related to machine condition monitoring, vibration monitoring, oil analysis and operations research.
PhD, Manufacturing Engineering and Operations Management - University of Nottingham
MSc, Computer Engineering - University of Balamand
- Research interests
- System optimisation
- Machine condition monitoring
- Vibration analysis
- Oil analysis
- Lean manufacturing
- Operations research
- Predictive Diagnosis System for Machine Faults Based on Vibration Analysis, Condition Monitoring, an official international publication of the British Institute of Non-Destructive Testing (June 2021).
- Optimization of Supermarket Checkout Counters Using Integrated Greedy Algorithms, BAU Journal - Science and Technology: Vol. 2: Issue 2. (2021)
- Emergency Vehicles Allocation Model for Urban City, BAU Journal - Science and Technology: Vol. 1: Issue 2. (2020)
- Online Quality Control Filteration System Used in Hydraulic Oil Process. European Scientific Journal. ESI. (July 2014).
- Predictive Diagnosis System for Machine Faults Based on Vibration Analysis. 1St World Congress on Condition Monitoring (WCCM 2017) London.
- Lean Production for a Freezing Potato Factory, 5th International Conference on Industrial Engineering and Operations Management. Dubai 2015.
- Genetic Algorithm Enhanced Neural Network Applied to Tool Condition Monitoring in Drilling Process. The CM 2013 and MFPT 2013, Krakow, Poland (2013).
- Cutting Tool Monitoring System for down Milling process using AI Methods. Conference on Computer and Automation Engineering (Singapore 2010).
- Cutting Tool Monitoring System for Down Milling process using AI Methods. International Conference on Advanced Computer Theory and Engineering (Egypt Sept 2009).
- Cutting Tool Wear Monitoring applying Support Vector Machines and Genetic Algorithms. International Conference in Advances in Computational Tools for Engineering Applications. ACTEA ’09. (NDU July 2009)
- Online Automated Condition Monitoring and Fault Detection of Machine Tools. 7th Automation and Computer Science Conference (Nottingham 2001).