Dr. Ashraf Sadek

The proliferation of data acquisition and sensor technology has made enormous amounts of data available to System Engineers (SE) which can facilitate real-time monitoring and enable the creation of reliability models improve operational and maintenance decisions. Gartner’s Analytics Ascendancy Model (AAM) identifies four types of statistical tools in an increasing order. Starting from descriptive statistics, it progresses to diagnostic methods to predictive methods and finally prescriptive analytics. Implementing Knowledge Management (KM) programs can help the SE convert the information from AAM into knowledge and use it to improve system performance. This may lead to a sustainable Machine Learning (ML) environment and autonomous system reliability adjustments.

Biography:

Dr. Ashraf Sadek is a Senior Welding Engineer at Ontario Power Generation. He has over thirty years of experience in power generation and petrochemical industries working in several roles. He obtained his BSc. in Mechanical Engineering from the American University in Cairo, his M. Sc. in Metallurgy from Cairo University, M. Eng. in Mechanical and Industrial Engineering from UofT and D.M from University of Phoenix. Dr. Sadek’s professional research interests focus on the use of knowledge management methodologies to achieve system and process reliability improvement.