Data Intensive Industrial Asset Management: Iot-Based Algorithms and Implementation

ISBN-13: 9783030359294 / Angielski / Twarda / 2020 / 236 str.

Farhad Balali; Narjes Nouri; Adel Nasiri

Spis treści

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.

O autorze

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 started working in the Center for Sustainable Electrical Energy Systems during his Master's program since 2014 and his Master's thesis titled "An Economical Model Development for a Hybrid System of Grid Connected Solar PV and Electrical Storage System". Currently, he is working on asset health management, statistical data analysis, and smart manufacturing systems. During his graduate program, he published six journal papers, two conference papers and one book chapter. Furthermore, he served as a reviewer of Renewable Energy, Energy, International Journal of Energy Research, Clean Technologies and Environmental Policy, Journal of Industrial Engineering International, IEEE Energy Conversion Congress and Exposition (ECCE), and International Conference on Energy Engineering and Environmental Protection.

Narjes Nouri received her B.E. degree in Industrial Engineering from the Khaje Nasir University of Technology, Iran, in 2012, and graduated as a M.S. in Industrial Engineering from the University of Wisconsin-Milwaukee, USA in 2015. Currently she is a Ph.D. student in Management Science in UWM and working as a Connected Enterprise Data Analyst for the Rockwell Automation. She worked as a teaching and a research assistant from 2013 to 2016. She has been working on different areas of research including water consumption optimization, facility location selection and predictive models for renewable energy systems. Her current research interests include statistical analysis, operation research and optimization and supply chain management. She seeks to improve the content of the educational and research experience to better match the needs of employers and the world. Narjes published five journal papers, one conference paper, and one book chapter during her graduate program.

Tian Zhao is presently an associate professor of computer science in the College of Engineering and Applied Sciences at UWM. His received his Ph.D. degree in computer science from Purdue University.  His research interests are primarily in programming languages, type systems, asynchronous programming, domain specific languages, and geospatial information systems. He has published many technical articles and conference papers and received several federal grants in related topics. He collaborated with his MS student, Nathan Roehl, in writing a chapter for this book.

Blurb

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.