Data Intensive Industrial Asset Management: IoT-based Algorithms and Implementation

From the Back Cover This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system.Provides a comprehensive reference, focused on the Asset Health Management Optimization Approach Using Internet of Things (IoT);Describes a data-driven optimization method, which considers the challenges raise by big data analysis;Enables a multi-objective approach, which includes the healthy index, reliability, availability, and cost, with respect to the optimization methods and computational restrictions which can have various applications. Product Description This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system. About the Author 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). Farhad Balali is currently a Ph.D. candidate in Industrial and Manufacturing Engineering at the University of Wisconsin-Milwaukee. Farhad was born in Tehran, Iran and studied Industrial Engineering at the K.N. Toosi University of Technology. He earned a Master's degree in Industrial and Manufacturing Engineering from the University of Wisconsin-Milwaukee in 2015. He