This book proposes the formulation of an efficient methodology that estimates energy system uncertainty and predicts Remaining Useful Life (RUL) accurately with significantly reduced RUL prediction uncertainty.
Renewable and non-renewable sources of energy are being used to supply the demands of societies worldwide. These sources are mainly thermo-chemo-electro-mechanical systems that are subject to uncertainty in future loading conditions, material properties, process noise, and other design parameters.It book informs the reader of existing and new ideas that will be implemented in RUL prediction of energy systems in the future.
The book provides case studies, illustrations, graphs, and charts. Its chapters consider engineering, reliability, prognostics and health management, probabilistic multibody dynamical analysis, peridynamic and finite-element modelling, computer science, and mathematics.
About the Author: Dr. Stephen Ekwaro-Osire is a full professor in the Department of Mechanical Engineering, and a licensed professional engineer in the state of Texas, USA. He was recently a Fulbright Scholar and the associate dean of research and graduate programs in the Whitacre College of Engineering. Dr. Ekwaro-Osire's research interests are engineering design, wind energy, vibrations, probabilistic prognostics and health management, and orthopedic biomechanics. He has more than 160 refereed publications, 45 of these in archival journals. He has supervised and graduated 32 doctoral and master's students. He is an active member of the American Society for Engineering Education, the American Society of Mechanical Engineers, the Society for Design and Process Science, the American Society of Biomechanics, and the Society for Experimental Mechanics.
Dr. Fisseha Meresa Alemayehu received his B.Sc. from Addis Ababa University, Ethiopia, his M.Sc. from TU Delft, The Netherlands and his Ph.D. from Texas Tech University, all in Mechanical Engineering. He joined the School of Engineering, Computer Science and Mathematics in 2016 as an Assistant Professor of Mechanical Engineering. Previously, he has worked as an Assistant Professor at Penn State University for two years and as Post-Doctoral Research Associate at Texas Tech University for one year. He was also a visiting scholar for more than two months in the National Wind Technology Center of National Renewable Energy Laboratory. His research areas include Probabilistic Prognostics and Health Management, Reliability of Renewable Energy Systems, Probabilistic Design, Probabilistic Multibody Dynamic Analysis and Teaching and Assessment Effectiveness. His research outputs have been presented in international as well as national Conferences and workshops and he has published research articles in International Journal of Mechanical Engineering Education, Journal of Mechanical Design, Journal of Computational and Nonlinear Dynamics and in several conference proceedings.
Prof. Dr. Aparecido Carlos Gonçalves graduated in Mechanical Engineering by University of Sao Paulo, Brazil in 1989. In 1993 he got a Master Degree, in Metallurgical Engineering, by University of Sao Paulo, Brazil. In 1997 he got his PhD in Mechanical Engineering also by University of Sao Paulo, Brazil. In the past he had actuated in Powder Injection Moulding and was referee of some Journal in the area. Today he is a Professor from University of State of Sao Paulo, Brazil where he was coordinator of Mechanical Project area for six years. He assembled a Laboratory of Oil Analysis and Tribology and now he is the Lab coordinator. He has Projects funded by the three main Grants agencies from Brazil: (FINEP - Fund for Studies and Projects; FAPESP- State of Sao Paulo Research Foundation and CNPq- National Research Council). Nowadays he actuates in Predictive Maintenance, Oil Analysis, vibration Analysis, Tribology and Mechanical Project. He is academic advisor and teacher the follow disciplines: Lubricant and Lubrication, Tribology, Predictive Maintenance, Solid Mechanical and Mechanical Design. He has oriented several graduate and undergraduate students.
