Data Intensive Industrial Asset Management

Adel Nasiri is presently Professor and Associate Dean for Research in the College of Engineering and Applied Sciences and Director, Center for Sustainable Electrical Energy Systems in the Department of Electrical Engineering and Computer Science at the University of Wisconsin-Milwaukee. He is also the site director for NSF GRAPES center. His research interests are renewable energy systems including wind and solar energy, microgrids, and energy storage. Dr. Nasiri has been the primary investigator of several federal and industry funded research projects and has published numerous technical journal and conference papers on related topics. He also holds five patent disclosures. He is a co-author of the book "Uninterruptible Power Supplies and Active Filters," CRC Press, Boca Raton, FL. As the associate dean, he has been leading several institute and center activities within the college of engineering and applied sciences. Dr. Nasiri is currently the Editor of IEEE Transactions on Smart Grid, Associate Editor of IEEE Transactions on Industry Applications, Associate Editor of the International Journal of Power Electronics, and Editorial Board Member of Journal of Power Components and Systems. He has also been a member of organizing committee for IEEE conferences including general chair of IEEE International Symposium on Sensorless Control for Electrical Drives (SLED 2012), Technical Vice-Chair for 2013, 2014, 2015 IEEE Energy Conversion Conference and Expo, and general chair of 2014 International Conference on Renewable Energy Research and Applications (ICRERA).

Principles of Internet of Things (IoT).- Asset Management and Maintenance Policies.- Statistical Data Analysis.- Descriptive, Diagnostics, Predictive, and Prescriptive Analysis.- Fault and Risk Analysis.- Predictive Failure Detection Algorithms.- Smart Device Function and Operation.- Data Driven Approaches.- Parameter Selection and Real Time Monitoring.- Edge and Cloud Computing.- Data Analytics and Visualization.- Artificial Intelligence for Predictive Maintenance.- Implementation Tools.- Case Studies.